Marketplace Lending – The Asian Experience

SMEs in Asia

Research paper for LendingCalc Date: September 19, 2017 Introduction Marketplace lending is blossoming in Asia, following the trend in China where over 2000 platforms are purportedly open for business. The majority of Asian platforms were founded in or after 2015, a few years after the Chinese boom, and are able to incorporate the lessons learned […]

SMEs in Asia

Research paper for LendingCalc

Date: September 19, 2017

Introduction

Marketplace lending is blossoming in Asia, following the trend in China where over 2000 platforms are purportedly open for business. The majority of Asian platforms were founded in or after 2015, a few years after the Chinese boom, and are able to incorporate the lessons learned from the Chinese market. This is also facilitated by the flow of Chinese capital into the region.

As domestic Chinese marketplace lending platforms are virtually closed to international investors by capital controls, this note will focus on South East Asia, where regulators welcome the participation of foreign institutional capital in marketplace lending, as in the United States or the United Kingdom. Nonetheless, marketplace lending assumes a different character than their Anglo-American counterparts due to divergence in regulatory regime and business environment.

The importance of regulatory regime

Platform is the putative strategy and business model of marketplace lending. Lately the word “platform” itself has taken on hallowed meaning in the fintech world, particularly since the Chief Executive Officer of Goldman Sachs proclaimed that “We’re a technology firm—we’re a platform” on Bloomberg Television.1 Unfortunately this newfound halo only obscures the reality that platforms are not equivalent across geographies, because it is their regulators and not the platform themselves that lay down the rules governing their structure.

To review the basics – A marketplace lending platform matches borrowers with lenders individually, hence also the term peer-to-peer (P2P) lending. Each loan may be matched to and funded by many lenders, hence the term crowdfunding; and each investor may match and fund many loans to diversify the investment. Exactly how the matching occurs is up to the regulator. The most relevant aspects are:

  1. Whether balance sheet lending (and hence leverage) is allowed;
  2. The responsibilities (and liabilities) to retail investors;
  3. The restrictions regarding institutional investors.

In the United States, Prosper and Lending Club are governed by SEC rules, and for all practical purposes behave like broker dealers. They originate the loans and sell securities called notes backed by the loans they originate under a program prospectus. They are technically securitizations, but unlike the CDOs of old, the notes do not correspond to tranched portfolios to enhance the credit rating; and no complex derivative is involved.

As for the three aspects above, broker-dealer designation implies that (1) there is no restriction to lend with its own capital; (2) sale to retail investors is allowed but strict SEC regulation applies and (3) institutional (or accredited) investors may participate.

To illustrate the divergence in the other extreme, China does not allow platforms to lend using their own capital, forbids the participation of institutional investors in marketplace lending and forces platform accountability to retail investors not by securities regulation by through strict disclosure policies. Curiously, under Chinese regulation, marketplace lending platforms are not technically financial institutions but are designated as credit information platforms.

Divergences in regulation directly impact the sustainability of the business. While not entirely obvious, marketplace lenders do spawn credit portfolios in the process, notwithstanding that the majority of which is eventually sold. Credit portfolios come with their long tails in loss distribution. How investors cope with the long tail and how platforms share this risk matter a great deal. The economics of a lending business, after all, cannot escape the logic of risk-return and the cost of funding. Peer to peer or not, lending has been about matching loans to funds for centuries. An unstable platform structure, begotten by clumsy regulation or a clumsy reading of regulation, will vitiate the revenues of the entire business. Structural risk is in fact the single most important risk factor in Asian platforms, and sadly, often poorly understood.

Large structural issues aside, smaller nuances, in cumulative effect, also influence the behavior of platforms. Lending cap is an illustrative example. While Singapore and Malaysia do not impose lending caps, Indonesian regulators have set a cap of SGD 200,000 per borrower, at least for now. Hence Indonesian platforms that are able to acquire a large number of borrowers consistently will generate portfolios with far greater diversification benefits.

The emerging market business environment

Small businesses in Asia face significant financing gaps. Such small businesses range from sole proprietors hawking goods in street bazars to more conventional small and medium enterprises (SMEs) with national or even regional supply chains. Measuring this financing gap is no simple matter. According to a study by IFC2, some 92 million SMEs in East Asia remain unserved or underserved.

As for access to personal credit, a large segment of the population remains unbanked or under banked. Some 55% of the word’s unbanked and underbanked population resides in Asia Pacific region3. The rise of marketplace lending (and fintech in general) is often hailed as the digital age’s cure to these chronic ills. The market size is immense.

That technology shall deliver the bulk of humanity from financial penury appeals to fintech idealists, and has rallied countless entrepreneurs under the financial inclusion banner. Reality, on the other hand, remains ever so unfailingly sober. Imperfect rule of law, absence of data infrastructure and deeply entrenched business practice define the business environment of marketplace lending in Asia.

Successful platforms adapt to this reality and thrive. The characteristics of loans generated reflect these adaptations, the most significant of which are negative enforcement mechanisms and partnerships.

Many Asian countries have just started constructing credit bureaus (e.g. Indonesia) or do not enable marketplace lending platforms to provide defaulting records to credit authorities (e.g. China). In some cases, the legal procedure to enforce default to prove financial fraud is too costly or burdensome to pursue. Hence negative enforcement mechanisms that seek to create negative consequences for unpaying borrowers supersede judicial options. These mechanisms need not be brutal or violent collection practices. For example, a notification to an SME borrower’s vendors, or the mere threat of one, could generally force the borrower into repayment or negotiation. Sharing a black list with other platforms or lending entities is also effective.

The use of partnerships is often motivated by negative enforcement, since partnerships generate a specific context where borrowers cannot default with impunity. For example, a partnership with an e-commerce platform that refers merchants as borrowers and freezes merchant accounts in case of defaults offers powerful negative enforcement. An agreement of program financing with a large manufacturer for its suppliers follows a similar logic. Some fintech practitioners coin the phrase “ecosystem play” to describe these mechanisms, but in essence such agreements rely on a partner’s ability to create and implement negative enforcement mechanisms and to bolster loan growth. Partnership loans therefore often feature prominently on Asian platforms, and well executed partnerships will provide platform investors with handsome risk-return.

At this point fintech enthusiasts may feel disenchanted that terms such as big data, machine learning or analytics have not found their way into the discussion. Such concern is partially warranted. The prevalence of data analytics in developed markets have reaped the benefits of data infrastructure efforts spanning decades by the private sector and the public sector. Lending Club could “easily” declare a credit score cutoff of 600 because Fair, Isaac and Company (now known as FICO) began building statistical credit scoring models in 1956, and because US public authorities have kept orderly records social security numbers to identify unique individuals, and because the US Postal Service began introducing the five-digit zip code system in 1963 which facilitated the construction of address databases. Most Asian marketplace lending platforms (with the possible exception of those in Singapore) must cope with lesser data infrastructure at the national level.

This does not mean that data analytics efforts in Asia will wither. The data landscape in Asia is actually more complex. In some areas such as credit bureaus, insufficient default histories simply take time to build up and will lag developed markets. In other areas such as the incorporation of social media driven payment data in anti-fraud modeling and supply chain logistics, Asia may leapfrog developed markets, as China has successfully demonstrated with payment infrastructure. As Asian countries fixate their attention on data infrastructure, marketplace lending platforms must navigate the data landscape as “work in progress”, but may also position themselves to contribute to the national data infrastructure through public and private sector partnerships.

For example, in Malaysia, private credit bureaus proactively solicit default data from marketplace lending platforms. In Singapore and Indonesia, more sophisticated private sector partnerships exist to share logistics, inventory and transaction data across regional supply chains between logistics companies, manufacturers and marketplace lending platforms to facilitate invoice financing. Data does exist in Asia, but who is willing to share what with whom is equally, if not more, important. Claiming technical superiority is inconsequential if the platform cannot sign agreements to unlock key data sources. The risk-return of loans generated by “automated” underwriting is not just about technical prowess.

Conclusion: What this means for investors

Marketplace lending in Asia offers investors fixed income exposures to a region of vibrant economic growth where conventional fixed income products are hard to come by, and without the volatility of marked to market instruments. The risk-return profile can meaningfully enhance any fixed income portfolio, but only for those who know to curate these platforms properly:

  1. Avoid platforms with structural risk.
  2. Seek out platforms well versed in regulation – and in dealing with regulators.
  3. Beware of platforms that boast analytics without explaining how they source data.
  4. Study how the platform implements negative enforcements.
  5. Look at how the platform handles partnerships, and who negotiates them.

The list is not exhaustive, but hopefully points investors in the right direction. If it reminds us of the business acumen of old, that should be no surprise. Lending is an ancient business, where risk emanates not from numbers but from human capriciousness. Those who can blend the latest analytics in capturing and measuring risk with the prudence to manage the psychology behind it will likely survive.


1 Bloomberg TV interview, June 2015
2 Closing the Credit Gap for Formal and Informal Micro, Small, and Medium Enterprises, IFC, 2013, p.8
3 See PWC DeNovo Q2 2016 FinTech ReCap and Funding ReView and datatopics.worldbank.org/financialinclusion/home

Author:

Terry Tse is a LendingCalc advisor focusing on the company’s strategic direction and global partnerships. He currently oversees global expansion at the largest B2B payment company in China, LianLian Group and serves as an adviser for the Southeast Asian P2P firm, Funding Societies. Formerly, Terry was the Chief Risk Officer at Dianrong, where he executed a ten-fold increase in loan origination for the P2P platform. Terry holds a B.S. in Mathematical and Computational Science and a M.S. in Financial Mathematics from Stanford University.

How to Give Millennial-Friendly Financial Advice

financial advice millennials

People often need financial advice that’s best suited to meet their needs, which differ from one generation to another. You cannot expect millennials to consume the same financial advice as baby boomers. How Millennials Manage Their Money In 2015, the number of millennials stood at 75.4 million while the number of baby boomers stood at […]

financial advice millennials

People often need financial advice that’s best suited to meet their needs, which differ from one generation to another. You cannot expect millennials to consume the same financial advice as baby boomers.

How Millennials Manage Their Money

In 2015, the number of millennials stood at 75.4 million while the number of baby boomers stood at 74.9 million. Millennials are described as anyone who is between 18 to 34 years old. Baby boomers, on the other hand, are between 51 to 69 years old. Between the two age groups is Generation X, which is made up of individuals who are 35 to 50 years old.

According to recent statistics, the median net worth of the top 20% of millennials is eight times higher than that of the lower 80% of this group. This is twice the ratio reported in the year 2000. Millennials also have a different saving culture compared to their predecessors. For instance, investors aged between 21 to 36 years of age hold more than half of their portfolio in cash with only 28% going into stocks. Older generations, on the other hand, held only 23% of their portfolio in cash with 46% going into stocks. It has also been revealed that 40% of millennials worry about their finances at least once a week.

Research studies have revealed that millennials are 1.5 times more likely to discuss their finances online. What is shocking is that a third of consumers aged between 18 to 29 years old have never had a credit card.

Growth of Tech-Driven Personal Finance Services

Over 80% of millennials own a smartphone and the number is steadily increasing. Over 89% of millennials usually check their mobile devices within the first 15 minutes of waking up. They also expect their financial institutions to be mobile and web-friendly. On the other hand, over 87% of millennials seek financial thought leadership through at least one social network.

What Millennials Look for When Picking a Financial Institution

Millennials have two main financial priorities; paying off their student loans and other debts, and saving for the future. On average, they spend 43% of their income to pay down their debts and put away 38% of their income as savings for the future. Three out of five millennials would like their bank to be a financial partner as opposed to just another business that feeds off their sweat. What is shocking is that only 32% of millennials feel like their bank understands them.

Before making a purchase, 84% of millennials always consider the values of a business or brand. If they are not in line with their own values, they always shop around for a more suitable brand.

10 Tips for Connecting with Millennials

  • Think Mobile First – To connect with millennials, you need to have a mobile app through which they can access your services. Alternatively, you should design a website that is mobile friendly.
  • Help Them Walk Before They Run – Financial growth is normally gradual, so you should develop solutions with low barriers of entry and can help millennials to manage multiple financial priorities as well as any urgent financial priorities they may have.
  • Give Credit a Makeover – Since millennials have different needs and values, you should reposition your credit products to align with those values and needs.
  • Make Financial Planning a Gateway
  • Consider Everyone a Competitor – Do not just look at the real world for competitors because millennials use social networks to get financial thought leadership, so you also need to offer better thought leadership through the same platforms.
  • Make it Rewarding
  • Make it Personal and Actionable
  • Educate Emphatically
  • Provide Holistic Solutions – To fully connect with millennials, you should provide solutions that take their priorities into considerations and can help resolve them.
  • Connect Visually


Ohio University Online

Author:

To learn more, checkout the infographic below created by Ohio University’s online Master of Financial Economics program.

Pros and Cons of Peer-to-Peer Lending for a Small Business

P2P lending

The peer-to-peer (P2P) lending business model enables small businesses (SMBs) to raise capital without necessarily approaching traditional lenders such as banks. More specifically, this business model gives entrepreneurs an online platform they can use to communicate and solicit funds directly from potential investors. To learn more, check out the infographic below created by Norwich University’s […]

P2P lending

The peer-to-peer (P2P) lending business model enables small businesses (SMBs) to raise capital without necessarily approaching traditional lenders such as banks. More specifically, this business model gives entrepreneurs an online platform they can use to communicate and solicit funds directly from potential investors.

To learn more, check out the infographic below created by Norwich University’s Online MBA program.

An Overview of P2P Lending

Modern P2P lending activities normally take place online where borrowers and lenders join P2P online platforms such as Prosper and Lending Club. People typically register as either borrowers or lenders. After joining, a borrower submits a loan application for review along with a plan detailing how he/she intends to spend the money raised. This stage normally determines whether a loan application is viable and aligns with the borrower’s investment strategies. Upon accepting a loan application, the review team publishes it on the P2P platform so that it is visible to all investors. In most cases, a loan application remains visible until it is fully funded or taken down by either the borrower or the P2P lending platform.

Differences Between P2P Lending and Traditional Financing

For starters, P2P lending platforms run their operations entirely online. As such, they typically have minimal personal contact between both borrowers and investors. Moreover, they require minimal personal information from the parties involved, which is considered good news for advocates of digital privacy. Another major difference between P2P platforms and mainstream lenders is that the P2P platforms do not lend their own money. Instead, they act as matchmakers that bring borrowers and lenders together. Nevertheless, P2P platforms offer investors some degree of assurance in the form of security notes that can be purchased on their sites. Unlike traditional financiers, P2P lending offers better interest rates, making the service highly attractive to value investors. In general, P2P lending offers better returns than financing opportunities offered by traditional lenders.

P2P Lending Platform Revenue Models

Like most web platforms, P2P platforms must generate revenue to cover their operational costs. Firstly, loan applicants are charged origination fees that vary depending on factors such as platform policies and the total loan amount. In addition, P2P lenders generate revenue by deducting and retaining a percentage of the interest charged to investors. You can think of these deductions as loan servicing fees. Peer-to-peer lenders also earn money via late fees.

Benefits

Compared to borrowing funds from a traditional lender, P2P loan application and processing is much faster. In fact, most loan applications are approved or denied almost instantly and those that make it through the approval stage are usually processed within two weeks. The collective funding approach underpinned by the P2P lending model protects investors from crippling financial losses. Small businesses also benefit immensely because they can easily access funding even with a poor credit history. Additionally, the minimal paperwork involved means fewer bureaucratic processes. Another major benefit is the lower likelihood of loan denial. Finally, the peer-to-peer lending model is predicated on a simplified customer experience. For instance, most P2P financing platforms are accessible via dedicated mobile apps, meaning investors can review funding applications while borrowers can check interest rates and application updates from the comfort of their homes.

Drawbacks

In spite of its benefits, the P2P lending industry is a relatively new financial model that is yet to be comprehensively regulated. This means investors may be unable to accurately determine the default risk of borrowers. It is also worth noting that borrowing funds via P2P lending platforms could substantially hurt your credit score because such platforms are set up to accept individual borrowers, not legal entities such as small to medium enterprises. This is particularly important because a low credit score would make it difficult for you to access financial services from mainstream lenders. P2P platforms also do not cultivate relationships with borrowers or lenders, which is the opposite of the know-your-customer approach adopted by banks. For this reason, there is little information about the P2P lending industry compared to the traditional financial sector. This aspect alone can have a negative impact on interest rates.

P2P Lending Statistics

The P2P lending industry dates back to 2005 when the first platform, Zopa, was launched in the UK. Shortly thereafter the Lending Club and Prosper, among others, were launched in the US. Since 2007, the volume of transactions via US-based P2P lending platforms has grown by 84% with loans worth $5.5 billion issued in 2014 alone. By 2025, the P2P lending industry will reach $150 billion or more. Some of the leading players in the P2P SMB lending space include Kabbage, Funding Circle, and OnDeck. OnDeck went public in December 2014 and boasts a market valuation of $1.8 billion, while Kabbage has raised $135 million in seed funding at a valuation of $1 billion. The Funding Circle has raised $150 million from investors at a valuation of $1 billion.

P2P Lending Trends

There is no doubt that advancements in information and communication technologies (ICTs) have boosted the fortunes of the P2P lending industry. As such, it is quite popular with tech-savvy Millennials who also account for 15.7% of small business owners. This was confirmed by a survey carried out by Morgan Stanley, which found that Millennials prefer faster, cheaper, and more convenient forms of credit. In addition, Millennials more than any other American age demographic favor web transactions conducted via mobile apps. Compared to Generation X, for instance, Millennials running small businesses are five times more likely to seek funding via P2P platforms.

Conclusion

The peer-to-peer lending business model is a novel capital-raising model that enables entrepreneurs to seek funds from a group of investors. Some of the key benefits of P2P lending include minimal paperwork, low interest rates, low risk of loan denial, simplified customer experience, and faster loan application processing. Nevertheless, P2P lending has drawbacks including lack of proper regulatory frameworks, minimal or no personal interactions, and high likelihood of negative credit score impact. In spite of these challenges, the P2P lending industry is growing fast, especially in the US where platforms such as the Funding Circle and Kabbage disbursed loans worth $5.5 billion in 2014 alone.

Norwich University Master of Business Administration Online

FinTech Alternatives to Short-Term Small-Dollar Credit — Helping Low-Income Working Families Escape the High-Cost Lending Trap

Illustration of “Center Post” Concept

Working Paper dated May 18, 2017 Abstract The focus of this paper is the most promising alternative to the current short-term, small-dollar credit (“STSDC”) system serving low-income working families –the rapidly growing U.S. financial technology software –or “FinTech”–industry. The paper begins by reviewing previous research on the underlying causes of demand for STSDC–payday loans, auto […]

Illustration of “Center Post” Concept

Working Paper dated May 18, 2017

Abstract

The focus of this paper is the most promising alternative to the current short-term, small-dollar credit (“STSDC”) system serving low-income working families –the rapidly growing U.S. financial technology software –or “FinTech”–industry.

The paper begins by reviewing previous research on the underlying causes of demand for STSDC–payday loans, auto title loans, bank overdraft protection and similar financial products–by low-income working families. The author discusses evidence that rising levels of monthly income volatility are creating a new set of liquidity management problems for families, and documents the adverse impact that reliance on STSDC for liquidity support has on working families, their communities, their employers and the economy as a whole.

The paper then describes regulatory interventions in this area and concludes that, despite considerable effort and some local success, none have materially curbed the expansion—or the adverse effects– of STSDC products nationally. The author discusses why banks are not likely to play a significant role in the STSDC market and argues that, in the anti-regulatory political environment following the 2016 election, private sector FinTech alternatives now offer the best opportunity to help low-income working Americans manage their day-to-day finances without resorting to STSDC.

The next section of the paper is an assessment of the potential for FinTech companies and products to provide a superior alternative to the current STSDC system. Using a variety of methods, the author identified relevant FinTech companies and classified them into six distinct categories. Then author contacted 50 identified companies and conducted interviews with senior management of 30 of these companies (several others were included in the study without interviews based on the prior knowledge of the author.) Based on these interviews and additional research, the author assessed individual FinTech companies and the identified categories of companies for “Utility” (defined as the ability of the products offered by a company to either provide a superior substitute for current STSDC products or an effective mechanism for consumers to avoid the use of credit products) and “Scalability” (defined as the potential for a company’s business model to support rapid penetration of the low-income working family market to serve a significant portion of low-income working families.) Using the assessments, the author distilled a detailed set of key observations about the strengths, weaknesses and challenges facing each of the FinTech categories, including the likely evolution of these categories over time.

The assessments show that FinTech companies in all of the categories, with one possibly temporary exception, are today providing products that have greater Utility than STSDC for low-income working families, and thus represent a meaningful improvement over the current STSDC system. One category—Digital Income/Expense Variability Management Solutions—was assessed as both the most Scalable and the highest Utility category measured in the study. A second category–Digital Credit Access/Cost Improvement Lenders—was also assessed positively in terms of both Utility and Scalability and should be able to provide significant amounts of alternative credit to low-income working families, subject to a number of important caveats. The other four categories of companies all had strengths in either Utility or Scalability, but would need to evolve further before becoming significant alternatives to STSDC for low-income working families.

The paper concludes that private sector adoption of a set of FinTech-centered alternatives to STSDC has the potential to shift a significant fraction of low-income working families away from reliance on the current STSDC system over time and to materially improve their financial resiliency and health, without the need for government financial support or new laws or regulations. The paper further argues that the employer channel is the best vehicle for disseminating FinTech products to low-income working families because of its potential to reach very large numbers of workers quickly with effective—and sometimes subsidized– liquidity and financial management solutions which also provide financial benefits to employers through reduced employee financial stress, improved employee engagement and satisfaction, lower turnover and lower absenteeism.

Based upon the author’s calculations, the use of FinTech products from the studied categories, alone or in combination, would be sufficient to manage a $700 to $980 maximum monthly negative variance (combining below average income and above average expense) in consumer income/expense, an amount sufficient in most instances to eliminate the need for a low-income working family to use STSDC. The author’s calculations further show that if these FinTech products were to become widely available, they would be able to address the Utility needs of a minimum of 4.7 million and a maximum of 15.6 million full-time workers in low-income working families. Collectively, the author believes that these FinTech products could benefit virtually all of the 10.4 million low-income working families and, indirectly, the 47 million individual members of those families, by reducing or eliminating reliance on STSDC.

The paper proposes a number of concrete steps that private sector and government employers, employee benefit providers, FinTech companies, other financial companies and non-profits can take to accelerate the adoption of superior FinTech alternatives to STSDC by low-income working Americans:

  • Employers (private and public) should adopt and subsidize employee financial health benefit plans that include the highest Utility products from FinTech companies.
  • Employee benefits intermediaries should support adoption of financial health benefit plans.
  • FinTech companies should broaden their offerings to incorporate the product capabilities of other FinTech companies into their own product offering for low-income working Americans.
  • Non-FinTech financial companies should adopt FinTech products to help improve their own customers’ financial health.
  • FinTechs and financial sector should resolve data governance Issues
  • The non-profit sector should advocate for FinTech benefits and data governance and consider subsidizing test cases.

The paper also sets forth public sector legislative/regulatory actions that could help accelerate adoption of FinTech alternatives to STSDC:

  • Congress should make employer contributions/subsidization with respect to Employee Financial Health Benefit Plans tax deductible.
  • State regulators should work collaboratively to reduce the burden of 50-state licensing and compliance on FinTech companies.
  • Federal and state banking regulators, with assistance from Congress as necessary, should make insured banking charters (national and state) available to FinTech companies with business models involving innovative digital deposit taking and other digital banking/lending activities that are (i) consistent with the purposes of banks generally but are (ii) inconsistent with the community banking format of locally-based customers and physical distribution coupled with a traditional mix of bank balance sheet and revenue components.
  • Regulatory and statutory uncertainty about permitted uses of “alternative data” should be resolved to avoid unnecessarily restricting the provision of high Utility FinTech products to low-income working families.

Introduction.

Every poor person must be allowed a fair chance to improve his/her economic condition. This can be easily done by ensuring his/her right to credit.”

–Mohammed Yunus

Most small-dollar loan borrowers can afford to put no more than 5 percent of their paycheck toward a loan payment and still be able to cover basic expenses…In the 35 states that allow lump-sum payday loans, repayment of these loans requires approximately one third of an average borrower’s paycheck.”

—Pew Charitable Trusts

Families who lose their home to foreclosure not only forfeit their existing equity, they also lose the opportunity to build savings, since they may be locked out of affordable credit for many years. Students who get loans to finance poor quality education programs find themselves with weak job prospects and heavy debt—debt that usually cannot be discharged in bankruptcy but can be garnished from Social Security benefits. Likewise, in most states creditors of auto and installment loans can seize property and garnish wages for up to 10 years when a borrower defaults on a high-cost loan that was never affordable. Over time, the damage to individuals becomes a community problem as significant spillover effects deplete the wealth of entire neighborhoods and communities.”

—Center for Responsible Lending

Ultimately, consumers have already rendered their verdict: they believe they benefit from the option of payday loans. So instead of restricting or eliminating payday lending markets through regulation, policymakers should seek to open them up to competition by repealing payday lending bans and regulations. The goal should be to maximize consumer choice and minimize the cost of short-term loan transactions. This will benefit economic growth generally and short-term borrowers in particular.”

—Reason.org

The day-to-day financial lives of low-income working Americans have become more and more precarious as income inequality has grown in the United States. Over the last 30 years, an increasing proportion of income has gone to the top end of the income distribution, and particularly in the top 1-5%, while the lower end has fallen badly behind.1 In a profound shift from the pattern of the post-World War II period, the benefits of productivity growth were not shared with labor in the form of wage increases. Stagnant real wages,2 the loss of many higher paying manufacturing and clerical jobs to technology and foreign competition,3 the decline of unions,4 and real estate price inflation in urban areas5 are just a few of the many reasons that have made it challenging for many low-income working people to “make ends meet” on a consistent basis.

Battered by economic forces over which they have little control, many lower-income Americans live on the edge. For the lowest quintile of the U.S. income distribution in 2014, housing absorbed 41% percent (50% for renters) of spending and the next 30% was split between food and transportation, with healthcare around 7% and clothing adding another 2%, leaving only a small margin for personal insurance, pensions, education, discretionary expenses and emergency expenses such as car repairs, unexpected healthcare costs, etc.6 Fully 36% of households in the lowest income quintile and 29% of households in the second quintile in 2011 said that they had trouble paying the rent, buying food, paying for child care or otherwise making ends meet at least once in the last year.7 When the balance between income and expenses is so tight, even the smallest change in monthly income or expenses can trigger a crisis.

And crises are becoming the norm for low-income working families. As we highlight below, a newly recognized factor–the rising volatility in Americans’ monthly incomes due to irregular work schedules, bonus plans and temporary bouts of unemployment–is triggering unprecedented financial management problems for people who already living on the edge. Today’s low-income working families experience more than a 10% increase or decrease in income in half the months of the year8 along with large (25%+/-) variances in monthly expenses.9 This level of volatility makes the task of managing family finances becomes nearly impossible without external liquidity assistance. Those working families who fail to find a way to fill the liquidity gap when income and expenses gyrate can find themselves excluded from access to housing, jobs, bank accounts, critical utilities and participation in the credit economy.

One of the consequences of flat real incomes, increased income volatility and regular expense volatility at the lower-end of the income distribution is severe personal financial stress.

Research demonstrates that financial stress does real damage by shortening lives, worsening health10 and impairing decision-making—especially financial decision-making—among stressed people.11 And financial stress is not just a problem for individuals and families– it affects employers’ bottom line too, as distracted and anxious employees have higher rates of turnover, absenteeism and generally perform worse on the job.12

The financial services industry has responded to the financial insecurity of low-income families by creating new forms of high-cost, short-term, small-dollar credit (“STSDC”) products that provide quick and easy liquidity injections for cash-strapped borrowers. These products, which include payday loans, auto title loans, bank overdraft protection and the like—have become a “de facto” liquidity support system for families dealing with the consequences of income disparity and volatility. They are easily accessible to consumers in lower-income neighborhoods lacking many other types of financial services. As we discuss in more detail below, these STSDC product (which are with one major exception provided by non-bank lenders) are expensive, poorly designed and demonstrably damaging to the lives of many of the consumers who use them. While these products clearly satisfy urgent short-term consumer needs, the negative individual, social and economic costs of this type of high-cost liquidity support are well-documented. There is little or no reason to believe that regular use of STSDC supports long-term positive change in the lives of financially stressed consumers or leads to sustainable economic growth.13

The growth in STSDC usage and the severe recession that followed the 2008 financial crisis brought increasing government and regulatory attention to the negative effects of STSDC on low-income families.14 This resulted in attempts by state and federal authorities to regulate some STSDC practices, culminating in the STSDC regulations proposed by the U.S. Consumer Financial Protection Bureau (CFPB) in 2016. Whatever one’s view of the wisdom or efficacy of these regulatory efforts, they reflected a political/societal view that the STSDC system was doing harm to many users and needed to be changed.

That political/societal view has now changed. The 2016 U.S. election marked the almost certain end of additional efforts to address the negative impacts of STSDC on low-income working families for the foreseeable future, at least at the federal level. The hostility of the current U.S. administration and Congress to the idea of consumer financial regulation generally and to the CFPB’s regulatory efforts in particular15 has made the prospect of consumer-focused regulatory change before 2021 highly unlikely.16

This means that market-based, private-sector alternatives now offer the best opportunity to help low-income working Americans manage their day-to-day finances without resorting to STSDC. Even if government leaders someday decide to deal with the economic issues underlying STSDC use, or again attempt to moderate the negative effects of STSDC through regulation, the existence of better private sector alternatives should make their jobs easier.

The focus of this paper is the most promising source of private sector alternatives to the current STSDC system–the rapidly growing U.S. financial technology software (“FinTech”) industry.17 We believe that innovative FinTech products have enormous potential to alter the STSDC landscape and provide better alternatives for consumers. FinTech companies are already designing market-based financial products to better address the short-term liquidity needs of low-income working Americans and products for the larger consumer market that can be adapted to the needs of low-income working people. Most FinTech innovation today is created by “start-up” companies funded by venture capital investors, although banks and other incumbent financial services providers are increasingly using their own resources to deliver FinTech capabilities for their customers. 18 While many of the FinTech companies we have studied are small today, we believe that some have the potential to achieve meaningful scale.

In the pages that follow, we will seek to examine in more the causes of STSDC demand and the shape of the current STSDC market, then evaluate emerging FinTech alternatives and assess their potential to reduce or eliminate reliance on STSDC by low-income working families.

We conclude that vigorous adoption of a set of FinTech-centered alternatives to STSDC by consumers and employers could shift low-income working families away from reliance on STSDC over time and materially improve their financial resiliency and health, without the need for government financial support or new laws or regulations. We believe that the FinTech products we studied would be sufficient to manage a typical low-income working family’s real monthly liquidity need and could eliminate need for use of STSDC by most or all of the 10.4 million low-income working families and their 47 million individual members.

We further conclude that the employer channel is the best vehicle for dissemination of FinTech products for low-income working families because of its potential to reach very large numbers of workers quickly with effective—and sometimes subsidized– liquidity and financial management solutions which also provide high value to employers through reduced employee financial stress. It is our view that if employers included FinTech alternatives to STSDC in employee benefit plans, they would not only help their employees manage their personal lives, they would also reap substantial bottom-line benefits from improved employee engagement and satisfaction, lower turnover and lower absenteeism.

We finish the paper by proposing a number of concrete steps that private sector and government employers, FinTech and other financial companies, non-profits and government can take to accelerate the adoption of these superior FinTech alternatives by low-income working Americans.

For purposes of this study we focused on the needs and concerns one specific demographic group–low-income Americans who are working in full or part-time jobs, or are participating actively in so-called “gig economy” work through companies like Uber. This demographic is a heavy user of the short-term small-dollar credit products that are the focus of our work. We are using the definition from the Working Poor Families Project’s 2013 paper entitled “Low Income Working Families: the Growing Economic Gap”,19 which defines low-income working families as those earning less than twice the federal poverty line.20 In 2011, the low-income threshold for a family of four with two children was $45,622 (it is slightly higher today). The number of low-income working families in the United States totaled 10.4 million and the total number of people in low-income working families totaled 47.5 million in 2011, including 23.5 million children. In 2011, low-income working families made up 32% of all American working families and 71% of those low-income families were classified as “working.” In the study, families are classified as “working” if they are significantly attached to the labor force (generally, those working at least 39 weeks during the previous year). Most of these families have bank accounts. Many low-income families include more than one adult who is responsible for meeting the household’s financial needs. In 2011, “about one-fourth of adults in low-income working families were employed in just eight occupations, as cashiers, cooks, health aids, janitors, maids, retail salespersons, waiters and waitresses, or drivers. Some of these occupations— especially those involving health care—are among the fastest-growing occupations in the country. Cashiers make up the single-largest occupational group, with nearly a million people in low-income working families in 2011.“21

The Rise of Income Volatility

It has long been recognized that monthly expenses are particularly volatile for low-income families, due not only to “emergencies” like car repairs and hospital payments but also to regularly occurring but unanticipated monthly increases in multiple expense categories.22 In a 2015 study of bank customers by the JP Morgan Institute (JPMI), the average participant in the bottom two income quintiles of the study experienced an expense increase of about 27% or decrease of 25% in 6 out of 12 months of the year.23 Expense volatility of this magnitude would be enormously challenging to manage at any income level.24

Other recent studies have shown that income volatility among low-income working families— long presumed to be low– is equally significant and in many ways more challenging to deal with.25 The same JPMI study showed the average participant in the bottom two income quintiles of the survey population experienced an income increase of between 11 and 14%, respectively, or an income decrease of between 9% and 11% in half the months of the year.26

Even median income individuals in the study experienced fluctuations of nearly $500 in monthly labor income across months.27

The leading cause for income variability cited by those surveyed by the Federal Reserve Board was an irregular work schedule, followed by being paid through bonuses or commissions, temporary unemployment, and seasonality of work. An irregular work schedule is cited almost as much as all the other reasons combined 28. Among the many reasons that low-income working families are facing irregular hours are the increasing use of employee scheduling software by employers,29 employer preference for part-time employees (who do not get benefits) and the rise of the “gig economy” of independent contractors with no fixed work schedules. The rising use of periodic performance bonus plans for hourly workers, which are inherently unpredictable, has added another new element to income variability.30

The 2015 JPMI study also demonstrated that monthly and annual income and expense changes did not move in tandem; there was only a slightly positive correlation between changes in income and changes in expense on an annual basis regardless of income level. The study found an even weaker positive relationship between month-to-month changes in income and month-to-month changes in consumption, with a slope of 0.06 that does not explain even 1% of the variance.31

JPMI’s study also indicated that consumers in the first two income quintiles would require $1,600 and $2,800 in liquid savings to handle normal monthly income and expense fluctuations, but had less than half of that amount available in transaction accounts at any given time.

This finding is consistent with other data that shows a general lack of savings or other sources of cash to help with liquidity challenges. More than half of American households earning less than $40,000 annually are not saving anything from their income.32 Only 34% of American households earning less than $40,000 could pay a $400 emergency expense with cash or a credit card repaid within a month.33 Fully 45% of the working-age population has no retirement savings account 34, and of those that do, 23% report dipping into their retirement accounts or borrowing from the account to cover short-term needs despite the risk of punitive tax treatment of early withdrawals. 35 Only 21% of Americans in the lowest quartile have a retirement account of any kind. 36And only 39% of the population has 3 months of expenses saved to protect from the impact of job loss or short-term disability.37

For purposes of our study of FinTech alternatives to STSDC and for ease of comparison we will simplify JP Morgan’s data and assume that the maximum amount of additional liquid financial capacity that a low-income working family would need in order to fully deal with monthly negative fluctuations in income and expenses is the range between $700 and $980. We estimated the estimated maximum monthly amount of additional liquidity required by low-income working families by using the JPMI 2015 analysis of the liquid assets needed to survive the maximum concurrent monthly income and expense shock for the bottom two income quintiles after deducting existing deposits ($1,000-$1,400.) We adjusted this amount downward by 30% to take into account single month voluntary capacity of families to increase income or reduce or defer expenses.38 We created our range based on two family after-tax income levels– $24,000 with a zero percent effective tax rate and $48,000 with a 20% effective tax rate.

Use of STSDC

In response to these liquidity pressures, many Americans—principally lower-income individuals–have turned to providers of various types of STSDC to help them handle variations in expenses and income, pay bills and avoid the consequences of rent and utilities nonpayment.39 These easy to access but enormously expensive products have become essential financial management tools for many lower-income working families.40

STSDC can be defined in various ways, but for purposes of this paper we will define it as general -purpose credit (i.e., not tied to the purchase of a product such as a car or furniture) with an original term of 9 months or less and in an amount of $2,000 or less. That definition covers credit products that are generally referred to as payday lending, bank overdraft protection, auto title lending, deposit advance and pawn loans and excludes products such as longer-term subprime installment loans, subprime auto purchase loans, “rent-to -own” contracts, point of sale finance, secured credit cards, as well as purchase money mortgages, home equity loans and contracts for deed secured by residential real estate. While many of these other credit products serve a similar customer base, they are distinct from STSDC in that they are in most cases not useful, or used, by consumers to deal with the types of “day to day” cash flow problems of the types described in this paper.41

 

Perhaps the most important aspect of STSDC products is that they do not require the user to have a good (or any) credit score. Thus, individuals with damaged credit from prior defaults or individuals with little or no prior credit history (young people new to the work force and recent immigrants, for example) can access these products.

A brief taxonomy of STSDC products follows below. Interested readers can access more detail about these products through the sources cited in the footnotes.

Payday Lending. A payday loan is a fee-based cash advance, typically two weeks in term, repaid in a single lump sum by post-dated check or ACH authorization out of the customer’s next paycheck, or “rolled over” for an additional fee until a subsequent paycheck. These are also sometimes also referred to as cash advance loans, check advance loans, post-dated check loans, or deferred deposit loans.

Emerged in the 1990s42
20,000 payday loan storefronts, many online websites

$38 billion lent annually (counting renewals as new loans) to 19 million payday loan customers43 who pay $9 billion per year in payday loan fees44 advance loans an even higher percentage, while long-term loans have much smaller monthly payments. In addition, many STSDC products contain a so-called “leveraged” repayment mechanism (e.g., an automatic draw on a bank account) which coerces the borrower into rolling over the entire amount of the original loan and incurring additional fees. The Pew Charitable Trusts, comment letter on CFPB’s Notice of Proposed Rulemaking for Payday, Vehicle Title, and Certain High-Cost Installment Loans, Oct. 7, 2016,

However, the value of longer-term installment loans as a device to handle day-to-day liquidity crises is, in the author’s view, limited when the frequency of household income/expense volatility episodes that are the Key Precipitating Factors for STSDC Use (as defined later in this paper) is so high—occuring in half the months of the year in the JPMI data cited elsewhere. It appears impractical for consumers to finance these types of recurring short-term needs with repeated long-term financing.

Payday borrowers typically spend over $520 in fees to repeatedly borrow $375 over several months45
“A typical two-week payday loan with a $15 per $100 fee equates to an annual percentage rate (APR) of almost 400%”46
Loss rate of approximately 60% for established payday loan businesses47

Deposit Advance Products. An advance paid directly into a bank account and repaid in a lump sum from the customer’s next direct deposit.

Linked to bank account
150% to 450% APR

$343 median daily balance48

Volumes are low as fewer banks are offering product due to regulatory pressures49

 

Bank Overdraft/Non-Sufficient Funds Protection (“bank overdraft protection”). A bank charges a fee (typically $35 per occurrence) to cover each incident of non-sufficient funds on each item (check, debit, ACH) drawn against a checking account. Most banks provide this service to checking customers, but customers must “opt-in” to gain coverage for non-recurring debit card transactions and ATM withdrawals.50

Bank overdraft protection fees make up over 2/3 total bank deposit account fees, averaging $250 per year for customers using service

Forty-Two percent of customers incur over $300 per year in fees51

Eight percent of bank customers incur nearly 75 percent of all bank overdraft protection fees52
70% of heavy overdrafters are employed and the average heavy overdrafter paid nearly a full week’s worth of annual household income in bank overdraft protection fees in the past year, and most people who expended a week of pay made less than $25,000 annually53

Ten percent of millennials overdraft more than 10 times a year54

 

Banks with over $1 billion in assets charged customers $11.16 billion in bank overdraft protection fees in 2015, accounting for 8.0% of profits. Small banks also offer these products so the banking industry total is considerably larger.55 Total fees typically make up 2% or more of total bank revenue.56

Bank practices frequently maximize incidence of fees in the daily settlement process by reordering transactions from large to small (thereby triggering an initial overdraft with one of the larger checks and adding additional fees from the smaller checks rather than paying most of the small checks before the overdraft occurs), by charging individual fees for each overdraft and by charging the same fee for all overdrafts regardless of size.

Put in lending terms, if a bank customer “borrows” $24 through an overdraft for three days and pays an overdraft fee of $34, the theoretical APR on the “loan” would be 17,000%.

Auto Title Loans. A small loan secured by the title to a car, truck, or motorcycle that the borrower already owns. The lender holds the legal title to the vehicle in exchange for a loan amount. If the loan is repaid, the title is returned to the borrower. If it is not repaid, the vehicle may be repossessed and sold.57

The Wall Street Journal has cited a much higher total industry figure: “So-called overdraft fees totaled $33.3 billion in 2016, up about 2.5% from 2015 and by 5.4% from 2011, according to Moebs Services Inc., an economic-research firm.” Andriotis, AnnaMaria and Rudegeair, Peter. “Bank Overdraft Fee Revenue Bounces Back ” The Wall Street Journal. 3/16/17. Available at:

 

8,000 auto title loan storefronts, many websites

$1,000 average loan size

Single payment or short installment term

$3 billion in fees annually for $2+billion in auto title loans extended to more than 2 million customers

Typical APR of 300%

Pawn Loans. Small loans collateralized by personal items such as jewelry, watches, cameras etc.58
$6 billion in revenues annually

10,000 pawn stores

$150 average pawn loan size, 1-4-month term, 20-25% per month, 15% default rate

7.4% of population has used pawn loans

Not surprisingly, as lower quintile incomes have stagnated and income and expense volatility have grown, the business of providing STSDC has become enormous and, by all accounts, profitable, attracting many new entrants over the years.59

Why Do Consumers use STSDC?

According to research by the Center for Financial Services Innovation (CFSI), consumers use STSDC principally to manage recurring expenses such as utility bills, rent, and food, which can be thought of as day-to-day household obligations.60 Approximately 42% of users of STDC borrowed to pay utility bills, and 41% borrowed for general living expenses such as food and clothing.61

CFSI’s research also confirmed prior findings showing that STSDC borrowers are generally less educated, have lower incomes, and tend to live in larger households in the southern U.S. Many users also report financial difficulties and indicate lack access to more traditional forms of credit.62

The CFSI survey also revealed that most STSDC consumers (66%) had no personal savings to look to as a source of cash flow assistance. Of those with savings, 45% still needed to use STSDC after using all their savings and the other 55% (19% of all STSDC users) used some of their savings or did not use their savings at all.63

CFSI’s research offers insights into the top reasons for the consumer’s funds shortage that precipitated STSDC use. The top four reasons are noted below (respondents could give more than one answer):

1. Timing Mismatch. Bill or payment due before paycheck arrived (38%)

2. Unexpected Expense. Unexpected expense (medical, car breakdown) (30%)

3. Unexpected Income Drop. Unexpected drop in income (lost job, hours cut, benefits cut) (28%)

4. Negative Cash Flow. General living expenses consistently more than income (33%)

 

In this study, we refer to the first three of these reasons for a funds shortage causing use of STSDC— timing mismatch, unexpected expense, or unexpected income drop” — as the “Key Precipitating Factors for STSDC Use.”

We have chosen to exclude the fourth category—consistent negative cash flow—from our study on the basis that no existing STSDC solution, and therefore no alternative to STDC, can be expected to ameliorate fundamental consumer insolvency for more than a brief period. Any effort to affect that problem will require changes beyond the scope of this paper.64

It is worth noting that CFSI’s respondents who used STSDC also reported taking other actions to deal with their financial problems. These included reducing spending generally, going without certain basic needs (food, clothing), deferring or skipping some bills, working more/earning more money and, unfortunately, using multiple loan products.

Finally, CFSI’s study showed that STSDC consumers most valued the speed of delivery, accessibility, and clarity of terms associated with the loan products they used when making the decision to borrow.65

Adverse Effects of Current STSDC System on Consumers, Communities and Employers

While the STSDC system provides immediate needed assistance to consumers who are facing daunting levels of income and expense volatility, the short-term benefits appear to come at a high cost in terms of longer-term financial instability.66

There are some commonalities among the different STSDC products that may explain why they are damaging many consumers. First, b orrower and lender incentives in STSDC are fundamentally misaligned, because the lender can succeed even if borrower fails to repay the obligation. So long as the borrower pays fees and/or high interest long enough, the ultimate repayment of the original loan principal isn’t necessary to generate profit. That is why STSDC products, while ostensibly short-term, often need to be “rolled over” a few times or used repeatedly in order for the lender to make a profit and why they can be profitable for the lender when the ultimate default rate on these products is so high. This incentive structure is very different from a traditional lender/borrower relationship, where the borrower and lender are aligned in both success and failure– both the borrower and the lender will only succeed if the borrower repays the principal on the loan.

Secondly, demand for STSDC is inelastic.67 This is due to the “urgent” nature of the borrowing and very poor competitive price discovery for borrowers at point of sale. STSDC competition is largely based on convenience rather than price or terms, which means that STSDC customers are “price takers” in almost all cases.

A third commonality shared by most STSDC products is the existence of a so-called “leveraged” payment mechanism whereby the lender can directly draw against the borrower’s bank account to secure payment unless the borrower renews the loan. When combined with lump sum repayments that can reach up to 50% of the borrower’s next pay check, “leveraged” payment mechanisms make it highly likely that an initial short-term loan will be renewed several times.68 This difference is significant because state regulatory and rate treatment of these loans are often dependent on their ostensibly “short-term” nature.

The negative consequences of STSDC use, which affect both performing defaulting borrowers, have been well documented69 and can include:

 

Debt traps–A long period of increasing indebtedness on an ostensibly short-term loan due to consumer renewal or “rollover” of the initial debt and incurrence of additional fees, or a high number of loans taken out per year, indicate the presence of a debt trap. As noted above, one of the most pernicious aspects of many STSDC products is the fact that the first STSDC loan is often unprofitable for the lender. Only after one or more rollovers does the loan become an economically positive transaction for the lender. “Leveraged” payment mechanisms and lump sum repayment help make these rollovers a frequent reality.

Excessive loan fees and interest—up to 400+% annual percentage rates (“APRs”) for most payday, auto title, deposit advance and pawn loans and equivalent amounts for bank overdraft protection fees. Despite arguments that these astronomical ra5es are necessary because of inherent credit risks and servicing costs, research suggests that payday loan businesses, for example, can continue to function while charging far lower rates than charged today through more efficient distribution strategies.70

Closure of bank account due to unpaid bank overdraft protection fees or deposit advance product balances

Costs resulting from default. If a borrower defaults absolutely on STSDC credit the adverse results include those typical for other types of consumer loan defaults, including wage garnishment, loss of transportation or other pledged assets, loss of access to housing, insurance, cable and cellphone plans, and problems with future employment due to adverse credit checks.71

There are also costs that extend beyond the individual consumers involved in the STSDC transaction. Frequently, third parties step in to help find a solution for the borrower when he or she can’t manage repayment personally. Friends and family are a frequent source of repayment assistance72 but bring with them a significant risk of “contagion” as the assistance-giver can often find him or her caught in a debt problem. A significant portion of the federal Earned Income Tax Credit (or EITC) payments for low-income taxpayers is likely used to repay STSDC every year,73 hardly the best use of taxpayer funds to assist working people74. Charities are also frequently called upon to help deal with the human toll of STSDC repayment problems. Communities also suffer where a significant amount of economic activity is concentrated around STSDC outlets (think of neighborhoods with a payday lender, a title lender, a check casher and an income tax preparer (for EITC) on every corner but with no supermarket in sight.)

The macroeconomic costs of the diversion of productive resources to support an inefficient and expensive system of STSDC are significant. One study found that the money payday loan borrowers spent on fees and interest led to a net drain of nearly $1 billion and over 14,000 jobs from the economy in 2012.75 A 2014 Howard University study of Payday lending in four Southern states noted that high costs Payday loans strip away a significant portion of disposable income from low- and middle-income families and, through a multiplier process, cause larger adverse effects on both the local and state economies in the hundreds of millions of dollars per annum, with the impact concentrated in the most vulnerable areas.76

 

As noted above, employers also pay a significant price—in terms of reduced productivity, absenteeism and turnover– for the financial stress caused by employee reliance on the STSDC system. While research in this area is still incomplete, there are very strong indicators that link employee performance and corporate financial results to levels of financial stress and financial health. 77

There is, surprisingly, no long-term study on the costs and benefits of the current STSDC system to individual borrowers or on the impact that regular reliance on STSDC has on the financial health of borrowers over time.78 Most academic research to date has been unable to conclude whether STSDC has any positive or negative impact on consumer welfare.79 Industry-sponsored research has largely focused on surveys of “customer satisfaction” which are inherently suspect given the funding of the survey and the coincidence of the survey with the satisfaction of an urgent consumer need at the point of sale.80 More generalized policy support for aspects of the current STSDC system has largely come from “libertarian” think tanks sponsored by allied corporate interests.81 The best-known academic economic defense of payday lending is Gregory Elliehausen’s 2009 study for the Board of Governors of the Federal Reserve and George Washington University, which concludes that payday loans are benign sources of debt financing for credit-rationed consumers.82 Other academics have sometimes concluded that the problems with payday lending are a function of insufficient competition rather than structural flaws with the product or with market solutions.83 And recently Lisa Servon from the New School has used her anecdotal experiences working in a payday lending store to draw attention to how personal service and “transparency” (meaning fees instead of interest) are key reasons why consumers use STSDC and similar services.84

Regulatory Interventions

Federal and state regulatory efforts to curb some of the adverse effects of the current SDSTC system have been uncoordinated and largely ineffective due among other things to:

Confusion and conflict about federal and state regulatory jurisdiction and oversight responsibility for various types of credit providers

 

Concerns about the consequences of limiting forms of credit on which so many Americans have become dependent

Successful industry lobbying efforts to protect successful and profitable business models

A product-by-product approach to regulation which has been undermined by the so-called “Balloon Effect,” as efforts to regulate one type of STSDC product diverts demand into other, less regulated, STSDC products 85

It is also worth noting, although the topic is beyond the scope of this paper, that decades-long efforts by both government and private sector institutions to improve consumer financial decision making through “financial literacy” education have been almost completely ineffective.86

State regulators today are still the primary overseers of all STSDC providers other than banks. State regulatory intervention in the STSDC market in the US in the 20th and 21st century has varied widely in approach and effectiveness and, today, the regulation of STSDC products remains a patchwork of conflicting statutory regimes.

According to the Pew Charitable Trusts, in 2014 most Americans, –55%– lived in the 28 states that imposed virtually no limits on payday lending. Payday loans were essentially unavailable in the 14 states and the District of Columbia that currently impose a 36% or less APR cap on payday lenders–a rate level too low, according to lenders, to support storefront payday lending.87 Eight other states imposed some limits on payday loan rates and practices, but payday lenders continue to lend actively in their states.88 Generally, online payday lenders must comply with the state law where the borrower makes his or her application, which means that borrowers in a prohibited state would have to apply from a location in a permissive state (or use an online offshore or tribal lender which is evading regulation entirely) to get a loan.89

“Restrictive states either do not permit payday lending or have price caps low enough to eliminate payday lending in the state. This rate cap is often 36 percent Annual Percentage Rate (APR). Generally, payday loan storefronts are not found in these states. This category includes states where deferred presentment transactions (post-dated checks) are not authorized, are not specifically exempted from general state laws on usury, or are explicitly prohibited by state statute. Twenty-nine percent of Americans live in the 14 states and the District of Columbia that have a Restrictive payday loan regulatory structure.

Hybrid states have relatively more exacting requirements, with at least one of the following three forms of regulation: (1) rate caps, usually around 10 percent of the borrowed principal, which are lower than most states but still permit loans to be issued with triple-digit APRs; (2) restrictions on the number of loans per borrower, such as a maximum of eight loans per borrower per year; or (3) allowing borrowers multiple pay periods to repay loans. Storefronts that offer payday loans exist in substantial numbers in these states, though the market may be more consolidated and per-store loan volume may be higher here than in less restrictive states. Sixteen percent of Americans live in the eight Hybrid states.

Permissive states are the least regulated and allow initial fees of 15 percent of the borrowed principal or higher. Most of these states have some regulations, but allow for payday loans due in full on a borrower’s next payday with APRs usually in the range of 391 to 521 percent ($15 to

$20 per $100 borrowed for a two-week loan). Payday loan storefronts are readily available to borrowers located in these states. Most Americans—55 percent—live in the 28 Permissive states.”

Available at: n%20regulation%20and%20usage%20rates/report/state_payday_loan_regulation_and_usage_ rates.pdf

 

A 2012 study by the Consumer Federation of America disclosed a similarly patchwork system of state laws covering auto title lenders, who were operating actively in 21 states in a product format that allows lending at triple digit APRs. Pawn lending is generally not subject to significant rate regulation.90

Research and advocacy groups such as the CFSI, The Pew Charitable Trusts and the Center for Responsible Lending have been active in recent years, with some success, promoting reforms at both the state and federal levels to reduce the burden of the STSDC system on consumers.91 While some state legislatures and voters have taken additional independent action over the last decade to curb “abusive” STSDC practices,92 and some parts of the STSDC industry have contracted as a result, the impact on the national market for these financial products has been relatively modest.

Federal law and regulations covering STSDC have been limited in scope and seem likely to remain so for the foreseeable future. In 2006, Congress passed the Military Lending Act, which in part forbade lenders from charging more than 36 percent APR on short-term loans (defined as loans of 91 days or shorter) to active military personnel or their families. According to the CFPB, that provision has been widely evaded by lenders, who responded by offering loans slightly longer than 91 days.93

More recently, the federal Consumer Financial Protection Bureau (CFPB) proposed regulations limiting certain business practices of payday, deposit advance, auto title and similar lenders,94

Specifically, the CFPB proposal included the following protections for users of STSDC:

Full-payment test: STSDC lenders would be required to determine whether the borrower can afford the full amount of each payment and still meet basic living expenses and major financial obligations. The proposal also made it more difficult for lenders to push distressed borrowers into reborrowing or refinancing the same debt and capped the number of short-term loans that could be made in quick succession.

Principal payoff option for certain short-term loans: Notwithstanding the “full payment test,” consumers could borrow a short-term loan up to $500 without meeting the test as part of the principal payoff option that is “directly structured to keep consumers from being trapped in debt.”
Less risky longer-term lending options: The proposal would permit STSDC lenders to offer two specified longer-term loan options, including a credit union loan product and a designated type of installment loan, in the latter case only if they agreed to limits on interest rates, fees and the number of loans offered per year.

Debit attempt cutoff: In an attempt to limit multiple bank overdraft protection charges by STSDC borrowers, STSDC lenders would be subject to limitations on the number of attempts at debiting the consumer’s account to collect payment for any loan covered by the proposed rule.

Consumer Financial Protection Bureau. Consumer Financial Protection Bureau Proposes Rule to End Payday Debt Traps. 2016. Available at:

The CFPB’s proposed regulations followed an extensive fact-finding effort by the CFPB and generated extensive public comment from industry and consumer advocates. The response to the proposal was generally negative, with industry objectors complaining that the proposal was unduly complex, burdensome to apply and would leave many consumers without an effective way to manage their finances [De Rugy, Veronique. Hurting the Poor Is No Way to Help Them: Payday Lending Rules Edition. 2016. Available at: and consumer advocates arguing that the proposal did not do enough to ensure that borrowers could afford these types of loan. statement-opposing-exceptions-cfpb-payday-ruleThe final regulation has not yet been issued and, at the time of this writing, the CFPB’s future– and the future of the CFPB itself– are in doubt. Marte, Jonnelle. “There is a fierce tug of war over the future of the CFPB.” Washingtonpost.com: Get There. 1/27/17 Available at:

and has indicated an interest in regulating bank overdraft protection practices.95 Both of these initiatives faced considerable opposition from lenders and in some cases from consumer advocates who felt the proposals did not go far enough.96 As discussed above, given the political environment in which this paper is being written—with the political party holding the Presidency and a majority in both houses of the U.S. Congress philosophically committed to an anti-regulation policy– the prospect of further federal regulation in this area is unlikely. In addition, the many ongoing challenges to the CFPB’s existence, governance structure and powers make it unlikely that either the proposed short-term credit regulation or rules on overdrafts will ever go into effect.

Banks are subject to a degree of Federal regulation when they offer bank overdraft protection and deposit advance Products. In 2009, the Board of Governors of the Federal Reserve System (FRB) amended Regulation E97 to prohibit institutions from charging bank overdraft protection fees for ATM and one-time debit card transactions, unless the consumer opts in or affirmatively consents to the institution’s overdraft services.98 Aside from these rules, Federal laws do not establish any maximum amounts of fees, including bank overdraft protection fees, which national banks can charge on an account. State laws generally impose no limits on state banks for competitive reasons.

While some of the larger consumer banks, under pressure from consumer groups, the CFPB and other bank regulators, have made positive changes to some of their practices—for instance eliminating “high to low” transaction ordering, the practice which was the subject of the most criticism by consumer advocates—the Pew Charitable Trusts most recent study showed that most of the largest U.S. banks with consumer checking accounts continue to charge at least $35 each time an overdraft is incurred, more than 40 percent of these banks process transactions from largest to smallest by dollar amount, and nearly 80 percent allow overdrafts on ATM and debit point-of-sale transactions.99

Bank deposit advance products, a type of consumer loan, are regulated to the same extent as other bank-provided lending products, and are not subject to specific limitations on rate. However, federal bank regulators have in recent years imposed “guidance” which effectively limits other product features typically relied on by the banks that provide these loans.100 As a result most large banks have exited this product.101

All in all, none of these regulatory interventions have materially curbed the expansion of STSDC products nationally. This result is not surprising given that the Key Precipitating Factors for STSDC Use remain urgent for consumers regardless of the regulatory environment, and the “Balloon Effect” means that product-by-product regulation appears unable to curve aggregate use of STSDC products.102

What About the Banks?

One key question remains. Where are the banks in all this? After all, virtually all STSDC users are bank customers (with the exception of pawn loans, STSDC products require a bank account to work.) But banks generally don’t provide products that compete with STSDC, other than bank overdraft protection. Why aren’t the banks, which already have all the data they might need to assess the financial lives of their depositors as well as the advantage of much less expensive funding, providing lower-cost and less damaging alternatives to STSDC?

 

The justification from the banking industry for this failure to meet customer needs is that regulatory pressures, publicity risks derived from the role of bank subprime lending in the 2008 crisis and the fear of public shaming from aggressive enforcement actions make any type of “subprime” consumer lending at high interest rates impossible for an insured bank. Some banks say that they would offer alternatives to STSDC to their own customers if they could avail themselves of a regulatory “safe harbor” for specific products and practices that would protect them from backlash.103

While the issues of bank risk appetite and bank regulatory reform outside of the FinTech sphere are beyond the scope of this paper, there seems little doubt that, if the regulatory “cloud” were lifted and banks elected to engage in lower-cost forms of short-term, small dollar lending, they could provide much-needed liquidity support a subset of their customers. But the question of whether banks really would enter this market in size is an open one. Some banks have indicated an interest in providing small-dollar credit, and that desire should be taken seriously. 104However, bank forays into high-risk consumer lending in the mid-20th and early 21st century generally had poor success, and there is a long history of banks entering and then exiting the business.

Banks typically invest in these businesses during the earlier parts of the credit cycle when losses are low, growth is high and bank funding creates a financial advantage, but don’t have the stomach to stick with them when levels of non-performing assets and credit losses emerge in the latter part of the credit cycle. The reasons for bank caution are sound—high volatility businesses like subprime consumer lending, if allowed to grow too large, can negatively affect bank credit ratings, increase borrowing costs, invite regulatory interventions and reduce a bank’s price-earnings ratios and stock price. The author of this paper was personally involved in scores of acquisition and disposition transactions over 25+ years involving banks and higher-risk consumer finance lenders, mortgage lenders, manufactured housing lenders, insurance premium finance lenders and auto lenders, and frequently observed this pattern.

Finally, it must be acknowledged that the banks have a major financial stake in the current STSDC system in the form of overdraft protection products, which produce 2+ percent of bank revenues.105

Methodology

Our research methodology followed a six-stage path.

Stage 1. Identification of FinTech companies potentially relevant to study goals. We used a variety of methods to identify potentially relevant FinTech companies.

a. Knowledge of author. We initially identified companies known to the author of the study, who is an acknowledged expert in financial services and FinTech has been following developments in this area for several years.

b. Knowledge of experts. We sought input from experts, which included individual employees of the CFPB, CFSI, The Pew Charitable Trusts, and venture capital and other firms active in FinTech investing.

c. “Tech Crunch” and other literature search. We searched the databases of Tech Crunch and similar venture-focused sites to identify companies.

 

d. Internet search. There is a well-developed ecosystem of online publications and conference materials covering the FinTech business. We searched the Internet for announcements, press reports, analytic pieces and other information that could lead us to previously unidentified companies.

While it is probable that we failed to identify some FinTech companies that could be relevant to the study (particularly very early stage companies), we have a high degree of confidence that we have identified the most significant companies relevant to the study goals as of the time of our research.

Stage 2. Initial Classification of potentially relevant FinTech companies. We began with a website review of all the identified companies to classify them into categories based on the information available on the websites.

Our taxonomy included several categories (and thus, companies) that we ultimately determined were not relevant to our study. The categories ultimately deemed the most relevant to the study (the “Relevant FinTech Categories”) were:

a) Digital Credit Access/Cost Improvement Lenders

Companies that lend money to consumers for short-term needs but seek to do so in a less costly/better structured manner than traditional STSDC and/or lend at a lower cost to people with “thin file”106 or “damaged file”107 credit profile

b) Digital Credit Builder Lenders & Service Providers

Companies that lend money to consumers for short-term needs but which, through their lending activities, primarily seek to help consumers improve their credit score to be able to access “standard” lower-cost credit in the future

Companies that are not lenders but focus on providing services to help (i) lenders identify credit-worthy consumers who would not otherwise be able to access credit on standard terms, and/or (ii) consumers improve their credit score to be able to access “standard” lower-cost credit in the future

c) Digital Financial & Cash Flow Management Software Solutions

 

Companies that provide mobile financial management software applications and online services to guide consumers toward financially healthy behaviors and outcomes, sometimes in conjunction with other services

d) Alt Digital Banks

Companies that, although technically not banks, facilitate mobile transaction deposit solutions for consumers through bank partners, often in conjunction with other services

e) Digital Income/Expense Variability Management Solutions

Companies that provide employers with liquidity and/or financial “smoothing” solutions to help employees manage income & expense variability

f) Digital Savings Solutions

Companies providing mobile apps that facilitate consumer savings for liquidity management and other purposes

The categories of FinTech companies that were ultimately eliminated from the study included

(1) companies engaged in FinTech-enabled longer-term installment lending to consumers with damaged credit and low- to mid-range credit scores on standard terms (i.e. lenders who did not either improved access, modified terms or lower costs to consumers), (2) bank online issuers of secured credit cards, (3) FinTech-enabled credit union loan products, (4) online “point of sale” lenders who provided immediate credit for online purchases, and (5) other FinTech companies with businesses that, while in many cases focused on a similar demographic, did not directly address the day-to-day small- dollar cash flow needs of low-income working families. The basis for the exclusion in each case was as follows. We concluded that standard high-cost installment loans, while a potentially valuable tool in consumer financial management, were longer-term liabilities and generally a poor fit for the day-to-day, small dollar, cash flow mismatch needs of low-income working families caused by repeated episodes of income and expense volatility. We concluded that bank-issued online secured credit cards, where the consumer makes a bank deposit equal in most cases to the amount of credit available to the consumer, did not provide significant assistance to most low-income working families that lack any savings with which to fund the card. We concluded that, although credit union small-dollar loan products could potentially be helpful to a subset of our target consumer population who were credit union members, an analysis of the credit union business and these alternatives was beyond the scope of our study. We concluded that “point of sale” lending for online purchases was also out of scope, given the limited utility of this type of lending to “day to day” small-dollar cash flow needs. The final group of FinTech companies that were excluded from the study include many interesting business models that could be useful and relevant to low-income working families— for examples companies focused on automating child support and food stamp payments, simplifying the cancellation of unwanted online subscriptions, provision of banking services to the unbanked, arranging online rent payment, student lending for millennials and provision veterinary loans—but which we determined not to be directly relevant to resolving the day to day short-term small dollar cash flow variability needs which are the focus of our study. 108

Stage 3. Contact and Telephonic Interviews. We contacted all of the companies (50 total) in the Relevant FinTech Categories through email and/or social media mail (primarily LinkedIn messaging). We conducted interviews with senior management of 30 of these companies during the period from January-April 2017. In nine cases, the author of this paper had sufficient knowledge about particular companies obtained through information available outside the purview of this study. In those cases, formal interviews were deemed unnecessary. Eleven other companies declined to participate in the study or never scheduled an interview or otherwise responded following initial contact.

Figure 1: Relevant FinTech Categories and Companies109

 

Appendix A includes a website link for each listed company and brief description of each company’s business and products.

The interviews we conducted with executives of companies in the Relevant FinTech Categories covered the following general topics: history of the company and founders, nature of products and product capabilities, target consumer (or business) segments, unique selling proposition for business, use of data in unique ways to identify/reach customers or otherwise conduct business, marketing/distribution focus, challenges in reaching target consumer (or business) segments, sources of revenue currently and in future, applicability of business model to low-income working families today or by expansion of current model and related challenges, predicted size of addressable customer base, plans to scale business and challenges to scale, funding ability (debt and equity) as a constraint on business, stage of current equity funding (seed, venture capital, institutional, etc.,) regulatory status, issues and challenges, and openness to partnerships with existing financial services providers, non-profits and government. We also sought specific information relevant to the “Utility” and “Scalability” factors discussed below. Many study participants voluntarily shared other information about their companies. Study participants could designate certain information about their business plans as confidential, subject to our ability to use the data for purposes of the study and present confidential data in formats that would not disclose the identity of the company in question.

Based on what was learned in the interviews, several FinTech companies were reclassified into categories that more accurately reflected the evolving nature of their business models.

Stage 4. Initial Assessment of Companies and Products. Based on our research and interviews, we assessed individual FinTech companies in the Relevant FinTech Categories across two dimensions:

  • “Utility” which we defined as the ability of the products offered by a company to either (a) provide a superior substitute for current STSDC products in managing Key Precipitating Factors for STSDC Use, or (b) provide an effective mechanism for consumers to avoid the use of credit products of any kind in managing Key Precipitating Factors for STSDC Use. For purposes of category (a) above we considered a product to be “superior” substitute if it provided substantially equivalent functionality at a significantly lower price or with a product structure that would be likely to avoid debt traps and other adverse outcomes.

In considering a company’s Utility in either of the two alternative meanings noted above, we took into account a time element, in that several companies described their products as part of an explicit multi-step path for the consumer culminating in Increased Utility through (i) the ability to access a superior substitute for STSDC (i.e., lower cost payday-type credit or credit on standard terms and pricing) and/or (ii) financial resilience through improved financial capacity to avoid STSDC. We also took into account whether a company’s products were explicitly marketed to, or directed towards, low-income working families and, if not, whether they appeared to be adaptable to that customer segment.

  • “Scalability,” which we defined as the potential for a company’s business model to support rapid penetration of the low-income working family market to serve a significant portion of low-income working families. In assessing Scalability, we considered various impediments to scale, including operational capability, technology, funding, regulatory structure, and length of sales cycle. We focused heavily on key revenue and expense drivers of the business model and on our assessment of whether the company appeared capable of generating an appropriate return on capital once Scale was reached.

In considering a company’s Scalability we also assessed whether, and to what extent, external assistance or subsidy—from employers, non-profits or government–would be required to serve a significant portion of low-income families either of the two dimensions noted above, or to accelerate the scaling process.

In certain cases, we were able to assess a company’s Utility, but not its Scalability, from publicly available information despite our inability to interview company management.

It is important to acknowledge that our assessments, although data-based, was influenced heavily by the judgment and experience of the author. We received information at various levels of detail from different companies, and relied on our experience in interpreting and weighing much of the data we received.

Figure 2 shows data relating to our Utility assessments for certain products offered by companies in the Relevant FinTech Categories. We assessed Utility based on our estimate (calculated as described above) that between $700 and $980 in additional liquidity would be needed for low-income working families at the $24,000 and $48,000 per annum earnings level to survive a maximum concurrent monthly income and expense shock. We concluded from this exercise that these FinTech products, alone or in combination, appear sufficient in most cases to manage negative monthly variances in income/expenses for low-income working families.

Stage 5. Assessment of Relevant FinTech Categories. Using our assessments of individual FinTech companies, we extrapolated our results into assessments of the Relevant FinTech Categories across the same two dimensions of Utility and Scalability.

We distilled key observations from the interview and assessment process about each of the Relevant FinTech Categories to further inform our conclusions and recommendations:

a) Digital Credit Access/Cost Improvement Lenders

I. Companies that lend money to consumers for short-term needs but seek to do so in a less costly/better structured manner than traditional STSDC and/or lend at a significantly lower cost to a subset of people with a “thin file” or “damaged file” credit profile.

II. The companies in this category vary considerably in size and experience, ranging from mature lenders with established track records covering several years to new startups with little history.

III. The products provided range from products similar to payday loans to various types of short-term (and in some cases longer-term) installment loans, credit cards and lines of credit. In all cases, the pricing of these products, although high in absolute terms, appeared clearly superior to STSDC alternatives. Average APRs charged by individual companies ranged from as high as 145% to as low as the 20-30% range for different lending products from different companies in this category. Some lenders charged APRs for loans to new customers that were as high as some STSDC products, although generally rates charged by these lenders declined over time if the customer performed and improved his or her credit profile.

IV. Product design features were generally superior and loan underwriting processes frequently appeared designed to be compliant with the proposed CFPB rules on short-term credit. One critical distinction between these lenders and STSDC providers is that they report loan performance to credit bureaus, allowing
borrowers to build a credit record. This is a significant benefit in the U.S., where taking on debt is the only path to a credit score.110 An adequate credit score can lead to credit on standard terms and, as we have seen, is critical in other areas as many employers, insurers, landlords and cable and telecom providers require credit scores in order to do business with consumers.111

V. Another important characteristic shared by many of these lenders is a focus on progressive credit improvement and cost reduction through performance over time. Lenders that follow this model offer an initial borrowing at a rate nearly as high as STSDC, but reduce the rate, and otherwise improve loan terms, as the borrower pays the initial loan and takes on and pays subsequent loans. In one case, the opportunity for rate reduction is built into the initial loan itself—if the borrower takes actions that improve his or her loan profile during the loan term, the rate on the loan is reduced.

VI. The combination of lower costs and improved product and underwriting features led to an assessment of relatively high Utility for this category of companies.112 There are some caveats, however. Some of the companies in this category are still very “high cost” lenders in absolute terms, which compromises Utility, although all provide their loans at lower cost (sometimes significantly so) than current STSDC providers. And, as described above, many offer progressively lower-cost products for good borrowers. In addition, some of the products these companies offer are installment credit longer in tenor than STSDC, which makes these products less effective in addressing repeated, smaller liquidity shortages.

VII. In most cases, these companies were lending directly to consumers under state lender licenses. Others used, or were considering using, a bank partner to originate loans sourced by the company. The bank then immediately sold those loans to the company. The use of a “fronting bank” partner allows the lender to avoid otherwise applicable state law interest rate limits, although there are ongoing legal controversies in this area.113

VIII. Distribution was primarily mobile or web-based, although one company still uses a large store network in Hispanic areas to source most of its volume.114

IX. All the companies in this category use some form of “alternative data” to identify potential customers and in their loan origination and, often, servicing and collection, processes. This alternative data is used in addition to the traditional data sources from credit bureaus that underlie FICO115 scores. The companies generally believe that alternative data helps them to identify a subset of “thin file” or “damaged credit” consumers with low (or no) FICO scores whom the companies believe are qualified for more traditional credit products with lower overall borrowing costs, as well as improving their ability to detect fraud—traditionally a major problem for online lenders. The use of alternative data appears to be essential to this business model.

X. “Pre-screening” of customers (which means determining in advance from available data which individuals are likely to qualify for credit) is generally viewed as essential by these companies, especially as non-pre screened online loan origination channels are often viewed as “adversely selected” for credit purposes, resulting in higher losses for loans originated through that channel.116 Frequently, channels such as direct mail, telephone, social media, standard advertising and partner-based marketing were used to drive high-potential “pre-screened” customers to the online application process. The level of pre-screening is often tied to the business model of the lender. Lenders who charge lower rates need to pre-screen more carefully in order to keep losses lower, while higher-rate lenders can lend to a wider range of borrowers with less strict screening methods. This may affect the Scalability of the business models of the more selective lenders.

XI. The availability of funding, and the cost of funding, for originated loans is a major potential constraint for individual lenders in this category (as it is for all non-bank lenders) although it appears that the more established companies with lending track records currently have access to adequate funding from banks and institutional lenders including hedge funds. Given the high absolute rates charged for loans, more established lenders in this category appear to be able to generate sufficient net interest income to ensure profitability even with high
expected defaults and losses, and therefore currently have adequate access to funding. However, funding availability for finance companies is a function of capital market conditions and credit performance. As of the date of this paper, none of these lenders (with one partial exception) have completed a full consumer credit cycle or managed through a protracted capital markets disruption. This means that the funding capacity of these companies have not been challenged by an extended period of high defaults and credit losses. A further discussion of this issue appears below.117

XII. In nearly all cases, companies in this category were accessing bank checking account data from consumers in order to improve their ability to select borrowers, service and collect loans and initiate transactions in the account to make loan payments. Access to the consumer’s checking account was typically arranged through a third party service such as Plaid or Yodlee which provides lenders with consumer transaction history, balance data, income history, identify verification and account authentication, as well as the ability to automatically
draw from the account to make loan payments. Access to this data was often critical to lending, servicing and collections decisions.

XIII. There currently is no legal requirement for a bank to allow a FinTech company to access customer data held by the bank, directly or through third parties. The question of “data right,” “data sharing” and ownership and control of financial data held by banks is a contentious one, with FinTech companies and banks both collaborating in areas of mutual interest118 and lobbying against one another where interests diverge.119 The CFPB, under the authority of Section 1033 of the Dodd-Frank Act, is currently seeking public comment about consumer access to consumer financial information, including access by entities acting with consumer permission, in connection with the provision of products or services that make use of that information.120 The resolution of this question will have a profound impact on all the FinTech companies we studied.

XIV. Regulatory issues for these companies are typical to all non-bank lenders, and include compliance costs associated with state licensing, limits imposed on rates and fees by state laws, costs associated with using a bank as originator to avoid state rate restrictions, compliance with ECOA, FCRA, TILA, FDCPA and other federal consumer protection laws.121 Because state laws differ in many respects, some of these companies must tailor their products to individual state rules.

XV. While individual companies in this category may find their growth limited by the funding available to their individual enterprise for the reasons noted above, and some companies will also be inherently less scalable by limiting their lending to a relatively small group of pre-screened customers who can qualify for lower rates, the category as a whole was assessed to have a good degree of Scalability in the current environment. We see no reason why companies in this category could not collectively provide $10 billion+ annually in loans over time, which would be less than a quarter of the currently outstanding payday and auto title balances noted above122 and—to use an apt historical comparison– about one-seventh of the outstanding loans managed by Household, Inc., the largest U.S. “traditional” non-prime consumer finance company, in 2000, before the build up to the 2008 financial crisis.123 The largest of the companies that we interviewed already has over $400 million in loans outstanding. 124 This assessment was tempered by our view that funding availability for the entire category (and other non-bank lenders) is likely to be heavily reduced, and costs of borrowing increased, during a severe credit downturn or financial markets dislocation. For that reason companies in this category were assessed as having lower Scalability than otherwise would be the case.

b) Digital Credit Builder Lenders and Services

I. Companies that lend money to consumers for short-term needs but which, through their lending activities, primarily seek to help consumers improve their credit score to be able to access “standard” lower-cost credit in the future.

II. Companies that are not lenders but focus on providing services to help (i) lenders identify credit-worthy consumers who would not otherwise be able to access credit on standard terms, and/or (ii) consumers improve their credit score to be able to access “standard” lower-cost credit in the future.

III. No lender in this category agreed to an interview for this study. Based on other sources of information described above, it appears that the lenders in this category focus on customers between those targeted by Digital Credit Access/Cost Improvement Lenders and “standard” FICO-based consumer lenders.125 While these companies are lending, in some form, to their customers, they are doing so specifically to assist customers raise their credit scores enough to become eligible for “standard” FICO-based consumer credit. For example, one company in this category will lend money to a consumer, with the loan proceeds deposited in a restricted bank account, which becomes available to the borrower only after it pays off the installment loan. The lender takes essentially no risk and the customer’s payment performance is reported to the credit agencies. If the loan is repaid in a timely manner, the customer’s credit score should increase and his or her cost of borrowing should decrease.126

IV. A key revenue source for these lenders is fees charged to a “standard” lender for preferred access to customers who have successfully used the companies’ “digital credit builder” products. Essentially they are being paid for identifying marginal credits that have improved their credit scores by working with the lender.

V. We were unable to definitively assess either Utility or Scalability of the lenders in this category due to our inability to interview the companies in this category, although based on our external research we believe that that potential for Utility is moderate and Scalability is limited, unless these companies either become full-service lenders themselves or are absorbed into existing full-service lenders.

VI. Non-lender companies in this category were largely (i) developing alternative data-based credit scoring solutions to identify “thin file” customers with potential to receive standard credit, and/or (i) identifying consumers with the characteristics identified by the alternative data-based credit scoring solutions, and (III) marketing the credit scoring solutions and/or customers to existing lenders. Several of the companies also market directly to consumers by offering services like a mobile financial management applications or a useful “alternative” credit score, as a way to gather financial data used to identify and score consumers, who then are approached by partner lenders.

VII. These non-lending companies appear to demonstrate Utility by helping some consumers qualify for lower cost credit on improved terms but their Scalability appears dependent upon whether their alternative data-based credit scoring solutions are robust enough to be incorporated into many other lenders’ credit processes.

VIII. Many companies in this category were accessing consumer bank checking account data from consumers in the manner described above. This access is critical to their business.

c) Digital Financial & Cash Flow Management Software Solutions

I. Companies that provide mobile financial management software applications and online services to guide consumers toward financially healthy outcomes, sometimes in conjunction with other services.

II. These companies have generally demonstrated strong ability to acquire customers through social media and other online channels by providing valuable financial management services to mobile-savvy consumers.

III. As a general matter, the financial management services offered by companies in this category include assistance in managing and paying bills, avoiding bank overdrafts, maintaining savings and achieving personal financial goals. Frequently, insights from behavioral economics and psychology are used in designing the user experience to “nudge” consumers and maximize the likelihood that consumers will make good financial decisions. These companies were generally assessed highly for Utility because of their real potential for assisting low-income working families manage income and expenses and avoid use of STSDC products.

IV. All companies in this category were accessing consumer bank checking account data from consumers in the manner described above. This access is critical to their business.

V. The biggest challenge that these companies face is accessing sustainable sources of revenue. In most cases, the company’s mobile application is free for consumers and does not itself generate revenue for the company. As a result, it appears that these companies will, if they wish to continue as stand-alone businesses, need to adopt business models which either (i) generate revenue from fee-based partnerships with other financial services providers such as lenders, banks or investment firms (some have already done this), (ii) generate revenue from the company providing deposit, loan or investment services itself, or (iii) charge the consumer a fee for the service.

VI. These companies share consumer “trust” issues with companies in the Alt Digital Bank and Digital Savings Solutions categories. This is especially so if they enter into fee-based partnerships with third party providers for lending or investment services. Customer acquisition for these companies can be costly due to the need to provide multiple human touch points to convince customers to do financial business with an unknown startup.

VII. Despite the high level of Utility demonstrated by their products, it appears likely that the revenue challenge faced by these companies will lead them to combine with companies in other categories or expand their services into other categories as described above. The very valuable product they provide seems particularly well suited to inclusion in the business model of companies in the Alt Digital Bank, Digital Savings Solutions, Digital Credit Access/Cost Improvement Lenders and Income & Expense Variability Management Solutions categories. This leads to a limited view of the Scalability of most of these enterprises in their current form.

d) Alt Digital Banks

I. Companies that, although technically not banks, facilitate mobile transaction deposit solutions for consumers through bank partners, often in conjunction with other services.

II. The business model of companies in this category involves providing “front end” banking services to consumers exclusively through consumer-friendly and easy to use mobile and/or online interfaces, supported by bank partner infrastructure.

III. Because of regulatory barriers in the U.S., these companies are currently unable or unwilling to become regulated banks themselves. They must therefore provide any deposit-related and payment services through a regulated bank or other financial intermediary, and compensate, or be compensated by, that intermediary for the costs and benefits of the services provided. As a general matter, companies in this category were therefore focused on managing issues of costs and control associated with their bank suppliers. Because demand deposits generate both fee income (from debit card interchange127 and bank overdraft protection fees) and a smaller amount of “spread” income (due to the funding value of low cost and highly stable consumer deposits) for the bank partner, the bank partner is willing to compensate the Alt Digital Bank for deposits and accounts gathered through the mobile banking application.128

These companies can also, if desired, provide loans (either directly or through partnerships with other lenders) to their customers, although this requires appropriate state and federal licensing and compliance. In making loans directly, they lack a key advantage of banks in that they cannot use the deposits generated by the company’s mobile application to fund loan balances, and must rely on other, higher cost, sources of funding if they wish to make and hold loans. They can also provide financial management applications and offer other traditional bank and investment products to their customers through third-party partnerships, again subject to appropriate licensing and compliance.

IV. Companies in this category have an offsetting benefit in that the cost of customer acquisition and service delivery through exclusively digital channels— at scale– is generally much lower than the cost of traditional banking acquisition and service delivery.

V. Challenges for these companies include building trust for a purely digital banking product, managing expenses associated with supporting multiple touch points (mobile, online, phone) for customers, maintaining the viability of the value sharing model with bank partner as a revenue generator. They will also face funding challenges if they become lenders.

VI. In their current state, these companies do not demonstrate significant Utility for purposes of our study. Because they rely in most cases on demand deposits to generate income (through payments by the partner bank of a portion of that bank’s debit card interchange, bank overdraft protection and spread income from the deposits), they generally focus on a demographic with higher income potential, more assets and higher debit card spending than most low-income working families. They also do not currently provide any loan products that could substitute for STSDC and serve low-income working families’ liquidity management needs. However, as competition intensifies and these companies seek to expand and add additional sources of revenue over time, we believe that some Alt Digital Banks may reposition their customer focus and add products more suitable for the low-income working family demographic, like those provided today by companies in the Digital Financial & Cash Flow Management, Digital Savings Solutions, and Digital Credit Access/Cost-Improvement Lender categories.

VII. A compelling case can be made that, over time, the Alt Digital Banks will consolidate both the Digital Financial & Cash Flow Management and Savings categories entirely due to their superior revenue model and the customer acquisition and retention benefits of the added capabilities for the Alt Digital Banks. This would become even more likely if the regulatory environment changed to allow them to become FDIC-insured banks themselves. The opposite could also occur if companies in those other categories add Alt Digital Bank capabilities. It remains an open question whether, absent becoming (or being acquired by) regulated banks themselves, Alt Digital Banks can effectively achieve scale in their businesses given increased investment by existing banks in mobile delivery and their continued reliance on (and payment for) the services of the existing banking and financial services infrastructure underlying their products. The experience of the so-called Neo Banks in the United Kingdom suggests that, even with a formal bank charter, this may be challenging.129
e) Digital Income/Expense Variability Management Solutions

I. Companies that provide employers with liquidity and/or financial “smoothing” solutions to help employees manage income & expense variability.

II. The companies in this category were, with one exception, providing solutions through employers (or quasi-employers of independent contractors receiving 1099 income in the “gig economy” such as Uber) as a form of employee benefit.

III. The solutions fell into two broad categories: (i) inexpensive alternatives to STSDC borrowing through the advance of earned but unpaid wages to employees or independent contractors, prior to a scheduled payday, on demand, and (ii) income stabilization solutions to support predictable employee income by “topping up” wages in below average pay periods and repaying the advance in above average periods. In many cases, the companies we interviewed were working on adding other financial management tools to their solutions to further support employees’ “financial health.”

One critical advantage these companies offer is that their products can be delivered without significant credit risk either to the FinTech company or the employer. This is particularly true in the case of the companies advancing already earned but unpaid wages through software imbedded in the employer’s payroll/benefits system, that generally have first call on the employee’s paycheck. The one company providing an income stabilization solution has a small degree of risk from employees who leave employment with a negative balance associated with a previous “top up” of wages, but this risk is also ameliorated by direct access to the employer’s payroll system to secure repayment.

V. As a result of the very low credit risk created by their products, companies in this category are able to charge fees much lower than any company that lends its own money to low-income employees, and bears the repayment risk. These companies typically either charge employers a user-based program fee for the service or charge employees a usage-based fee. In most cases, the cost of the service is paid, or subsidized, by the employer. One company charges employees $5 per $500 in earned but unpaid wages advanced (this is sometimes employer-subsidized), which can be compared to the typical $15 per $100 charged by a payday lender. Other companies’ fees are entirely borne by the employer—the employee pays nothing to use the service.

VI. Both approaches were assessed as high Utility, with the first category (advance of earned but unpaid income) providing a clearly superior substitute for STSDC and the second category (income stabilization) assisting with avoidance of the need for STSDC products.

VII. Companies were generally promoting their products/services as a way for employers and quasi-employers to help employees manage income and expense variability, avoid STSDC and thus improve employee “financial health” or “financial fitness,” which is an area of focus in the employee benefits arena.130

Expected benefits for the employer included improved employee performance, lower turnover and lower absenteeism.131

VIII. Companies in this category generally were concerned about the long sales cycle associated with corporate purchases of their products/services. Companies were pursuing different distribution strategies to reach employers, including direct sales, integration with payroll providers, integration with employee benefit providers and integration with “time and attendance” software providers.

IX. Companies were also focused on delivering the simplest possible integration of their solution with employer or partner systems and keeping the timing short and the cost of installing the products/services as low as possible. Another area of focus was education and the promotion of the product to employees once it is made available through the employer. Companies generally felt that the implied employer endorsement helped speed employee adoption of their products.

X. All companies in this category were accessing consumer bank checking account data from consumers in the manner described above. This access is critical to their business.

XI. Regulatory issues were not generally viewed as a significant concern at the present time, as the companies had generally structured their solutions in a manner which, in the companies’ view, do not require state licensing as lenders or loan brokers despite some “loan-like” aspects of their customer interactions.

XII. These companies generally were assessed very highly for Scalability as a single large installation could assist several hundred thousand low-income employees although the long corporate sales cycle means that individual companies in this category would likely add consumer customers slowly and in “chunks.” Nevertheless, the potential is large. If 25% of the employees of Wal-Mart, Yum! Brands, McDonald’s, UPS, Kroger, Target and Home Depot 132 were to use the services of these companies in place of STSDC, it would remove close to 1.2 million individuals from the STSDC system. Adoption by the U.S. military would also bring quick scale. If 30% of active military and reserve personnel (who totaled more than 2.1 million in 2016) were to use these companies’ services, it would remove another 630,000 individuals from the STSDC system.

XIII. It also seems probable that these companies will seek to become “center posts” for a broader set of employer-sponsored financial health benefits and will add additional FinTech products that employers could offer to assist employees with financial challenges. Among these products could be some currently provided by the Digital Financial & Cash Flow Management, Digital Savings Solutions and Alt Digital Bank categories.

f) Digital Savings Solutions

I. Companies providing mobile apps that facilitate consumer savings for liquidity management and other purposes.

II. Companies in this category use a variety of methods, including free financial management software, psychological/behavioral “nudges” and “gamification” strategies, to attract customers and assist them in setting and reaching savings goals through mobile interfaces. Like the Alt Digital Banks, these companies are generally unable to become regulated banks themselves. They must therefore provide any deposit-related services through a third-party regulated bank, and compensate, or be compensated by, that intermediary for the costs and benefits of the services provided. Because savings deposits generate “spread” income (due to the lower cost and higher stability of deposits compared with other funding) for the bank partner, the bank partner is willing to compensate the Digital Savings Solutions provider for any deposits gathered through the mobile application. Unlike the demand deposit accounts gathered by Alt Digital Banks, savings accounts do not generate debit interchange or bank overdraft fee income for the partner bank, which is generally a larger source of revenue.

III. All companies in this category were accessing consumer bank account data from consumers in the manner described above. This access is critical to their business.

IV. Because of the simple, “savings only” indirect business model, these companies should theoretically be able to operate at lower cost levels than many of the other companies in the study. However, these companies also share “trust” issues with companies in the Alt Digital Bank and Digital Financial & Cash Flow
Management Software Solutions categories. Customer acquisition for these companies can thus be costly due to the need to provide multiple human touch points to convince customers to do financial business with an unknown startup. One of the companies in this category is using “gamification” to attract savers less expensively by providing “lottery-like” prizes for positive savings behavior. This approach—or other novel approaches– could potentially obviate the “trust” and cost of customer acquisition issues.

V. The Utility of accruing savings through one of these companies is high, as long as it does not merely substitute for existing savings through traditional bank channels. This is particularly so if savers are encouraged to view additional savings through one of the Digital Savings Solutions companies as “revolving savings”133– a short-term liquidity tool for managing Key Precipitating Factors for STSDC Use rather than a “long-term” retirement-focused asset.134

VI. The Scalability of these companies appears to be limited by two things. First, as a general matter, companies in this category, which rely on the support of traditional banks to deliver the deposit products they offer to customers, were focused on managing issues of costs and control associated with their bank suppliers. Revenue is entirely based on the amount third-party bank partners are willing to pay for deposits gathered. All of these companies were founded during a period where deposits were priced at or near zero; if general interest rates rise (they appear to be on an upward trend as of the date of this paper) the value of savings deposits for third party banks and the price they will pay for access to those deposits could change radically. This issue is exacerbated by the fact that all such deposits (like those of Alt Digital Banks) are considered “brokered deposits” for bank regulatory purposes, which in many cases reduces the value of such deposits to the receiving bank.135 Second, the savings deposit product is neither unique nor technologically complex. There is nothing preventing existing banks from reproducing the product features offered by Digital Savings Solutions providers today and using them for their own deposit gathering activities. Substitution may be more limited for companies with a unique approach (such as the “gamification” example noted above.)

VII. It appears likely that the revenue challenge faced by these companies will lead them to combine with, or become, companies in other categories. The very valuable high Utility product they provide seems particularly well suited to inclusion in the business model of companies in the Alt Digital Bank, Digital Financial & Cash Flow Management Software Solutions and Digital Income & Expense Variability Management Solutions categories. This leads to a limited view of the Scalability of these enterprises in their current form.

Stage 6: Estimated Size of FinTech Product Market for Low-Income Working Families.

Building on our conclusion above that FinTech products from the Relevant FinTech Categories, alone or in combination, appear sufficient in most cases to manage negative variances in income/expenses for low-income working families, we attempted to assess the potential size of the market for these products among low-income working families.

We calculated the estimated number of full-time workers in low-income working families who could reasonably be expected to benefit from products from the Relevant FinTech Categories, if those products were to become widely available. The calculations are shown in Figure 3.

 

Figure 3: Calculation of Addressable Market—Low Income Working Families

 

Based on these estimates, we believe that individual FinTech products would be able to address the Utility needs of a minimum of 4.7 million and a maximum of 15.6 million full-time workers in low-income working families. Collectively, we believe that these FinTech products could benefit virtually all of the 10.4 million low-income working families and, indirectly the 47 million individual members of those families.

General Conclusions.

  • As described above, there is a clear need for more effective and lower cost (financial, social and economic) alternatives to the current STSDC system due to:
    • The documented liquidity and income/expense variability challenges facing low-income working families,
    • The negative personal consequences for many may low-income working families who rely on STSDC to deal with liquidity issues, and the significant negative economic and social consequences of high STSDC use,
    • The largely ineffective regulatory attempts to limit the negative consequences of STSDC while continuing to provide access to liquidity support for low-income working families, and
    • The limited likelihood of additional government action in the regulatory sphere (or, for that matter, government action to change the underlying economic and social factors that lead to continuing reliance on STSDC by low-income working families) unless and until the political situation changes in the U.S.
  • Private sector adoption of a set of FinTech-centered alternatives to STSDC has the potential to shift a significant fraction of low-income working families away from reliance on the current STSDC system over time and to materially improve their financial resiliency and health, without the need for government financial support or new laws or regulations.
    • We believe that the use of FinTech products from the Relevant FinTech Categories, alone or in combination, would be sufficient to manage a $700 to $980 maximum monthly negative variance (combining below average income and above average expense) in consumer income/expense, an amount sufficient in most instances to eliminate the need for a low-income working family to use STSDC.
    • If FinTech products from the Relevant FinTech Categories were to become widely available, the individual FinTech products would be able to address the Utility needs of a minimum of 4.7 million and a maximum of 15.6 million full-time workers in low-income working families. Collectively, we believe that these FinTech products could benefit virtually all of the 10.4 million low-income working families and, indirectly, the 47 million individual members of those families, by reducing or eliminating reliance on STSDC. The importance of this conclusion is highlighted by a comparison to the total number of unique payday loan borrowers (12 million) and auto title loan borrowers ( 2+ million) in a typical year.

  • In addition, we believe that the employer channel is the best vehicle for dissemination of FinTech products from the Relevant FinTech Categories to low-income working families because of the superior Scalability of the employer-based model as well as the potential for rapid growth and employer subsidization. The size of the addressable market for employer-based FinTech solutions is very large. In 2014, almost 65 million Americans, or a little over 41% of wage earners, worked in jobs paying between $15,000 and $50,000 (a reasonable proxy for low-income working families.) 1 Over 55 million individuals worked for companies with more than 500 employees (47 million of these worked for companies with over 1,000 employees.) If superior FinTech-enabled alternatives to STSDC were to reach only 15% of the workers employed by large companies, 8.25 million employees would be better off. If these alternatives reached 40% of those employees, the number helped could rise to 22 million.
  • FinTech companies in all the Relevant FinTech Categories, with the possibly temporary exception of Alt Digital Banks, are today providing products that have greater Utility than STSDC for low-income working families, and thus represent a meaningful improvement over the current STSDC system.
  • The FinTech companies ranged from pure B2C direct models to pure B2B, with several that could be described as B2B2C because they market their services both to employers and employees. Many different sales and distribution strategies are being followed, including word of mouth/viral, social media, direct mail, TV advertising, direct sales forces and indirect sales through third party partners. While the B2B models in the Digital Income/Expense Variability Solutions category rate the highest for Scalability, several B2B models in other categories also appeared highly Scalable. Scalability and obstacles to Scalability were a major focus of management in virtually all cases. This is typical for early stage venture-backed companies.
  • One category—Digital Income/Expense Variability Management Solutions— was assessed as both the most Scalable and the highest Utility category measured in our study. Companies in this category also appeared to have the strongest business models

Figure 4: Category Highest in Utility and Scalability

  • Companies in the Digital Income/Expense Variability Management Solutions category, due to their focus on providing their products through employers as part of an employee benefit solution (a “Financial Health Benefit Plan”), appear to have the greatest potential of any category to create consistently Scalable and high Utility business models, which could reduce costs for employers 136 while having a major impact in increasing the “financial health”137 of low-income working families. This category provides both the least expensive and most immediately useful substitutes for STSDC and an important tool to help manage the Key Precipitating Factors for STSDC Use in a manner to avoid the need for STSDC . By including FinTech alternatives to STSDC in employee benefit plans, employers would not only help their employees manage their personal lives, they would also reap substantial bottom-line improvements through increased employee engagement and focus, lower turnover and lower absenteeism. As a result, we believe that the employer channel is the best vehicle for dissemination of FinTech products for low-income working families.
  • The employer channel also provides the opportunity for FinTech companies to grow by adding other financial products and capabilities, delivered through the Financial Health Benefit Plan, which are designed to improve employee financial health and reduce corporate costs associated with employee financial stress. The biggest challenge identified for this category is the extended sales cycle for sales of software solutions to employers (either directly or indirectly) through a B2B business model.
  • In addition to high assessment for Utility and Scalability, the FinTech companies in the Digital Income/Expense Variability Management Solutions showed significant potential for becoming the “center post” of a more extensive set of financial health solutions delivered through a Financial Health Benefit Plan. It seems likely that many of the services now provided by companies in the Digital Savings Solutions, Digital Financial & Cash Flow Management Software Solutions and Digital Credit Builder Lenders and Services categories could migrate to the employer channel pioneered by companies in this category.

Figure 5: Illustration of “Center Post” Concept

  • Digital Credit Access/Cost Improvement Lenders, collectively, are also assessed positively in terms of both Utility and Scalability and should be able to provide significant amounts of alternative credit to low-income working families. There are a number of caveats, however. Some of the companies in this category are still very “high cost” lenders in absolute terms, which compromises Utility, although all provide their loans at lower cost (sometimes significantly so) than current STSDC providers and most provide a path towards improved rates and terms over time. The ability of these lenders to charge consistently lower rates over time will be the test of whether they provide truly high or merely acceptable Utility to low-income working families. The companies in this group charging the lowest costs are also the most selective, which affects their Scalability. All companies in this category are also subject to material risks associated with their funding model—which is dependent on the willingness of institutional lenders of various types to advance funds– during a credit or financial crisis. These companies have not yet demonstrated consistent credit and funding performance through an entire credit cycle. History has shown that institutional-funded lenders often suffer badly when the credit cycle turns or funding markets become unstable.138 Individual companies in the category are likely to be limited in their individual Scalability by these funding and credit performance constraints, although the existence of multiple competing lenders should reduce the overall category risk associated with funding and credit. Interestingly, the best solution for the funding issue would be for these lenders to become deposit-taking banks, an outcome that, as noted above, does not seem likely any time in the immediate future due to regulatory and shareholder concerns.139 Nevertheless, these companies’ business models currently appear viable and we see no reason why, absent a near-term recession or funding crisis, these companies collectively could not lend more than $10 billion annually over time on superior terms to current STDC models, and remove an equal amount of STSDC, and related higher fees and costs, from the system.

Figure 6: Credit/Funding/Cost Uncertainty

  • Our review suggests that some of the non-lending FinTech companies in the Digital Credit Builder Lenders and Services category are likely to pivot towards becoming lenders themselves or may be absorbed by existing lenders or credit information providers, as they appear to lack the consistent revenue base necessary for Scalability, or be absorbed into the Digital Credit Access/Cost Improvement Lender category. Others may succeed as moderately scaled independent businesses.

  • Three other Relevant FinTech Categories—Digital Savings Solutions, Alt Digital Banks and Digital Financial & Cash Flow Management Software Solutions— also appear likely to coalesce into a newly combined business category centered around an “expanded’ Alt Digital Bank business model (possibly evolving into an insured bank in the future) as they broaden their product set to create new digital banking products and seek consistent sources of revenue from these new products. Companies in two of the three pre-existing categories—Digital Savings Solutions and Digital Financial & Cash Flow Management Software Solutions– share good, and in many cases, improving, levels of Utility with low to moderate Scalability due largely to lack of revenue. In many respects, these two categories can be characterized as FinTech “products” rather than fully developed business model. Alt Digital Banks, which generally have a B2C mobile distribution model and a focus on millennial customers, are currently low Utility despite having relatively high Scalability due to a strong source of deposit-based revenue. The low Utility assessment is largely due to Alt Consumer Banks’ focus on higher income customers (who generate higher levels of interchange income than low-income working families) and the fact that they do not yet provide either loans or digital financial management solutions for low-income working families. However, if the Alt Digital Bank business model were to be combined with the models of the other two categories, the combined business model could potentially extend to cover at least part of the low-income working family population and gain significantly greater Utility while remaining Scalable.

Figure 8: High Utility/Scalability Model May Emerge from Combined Categories

  • Additionally, the high Utility products provided by companies in the Digital Financial & Cash Flow Management Software Solutions and Digital Savings Solutions categories would appear to be attractive additions to FinTech businesses in other categories (as well as banks, credit unions and other traditional financial services providers), as they support behavioral changes that can improve the financial health and resiliency of low-income working families over time.
  • Virtually all of the FinTech companies surveyed were, to a greater or lesser degree, dependent upon the ability to access bank checking account data from consumers. In many cases, their business model also requires gaining permission from the consumer, and cooperation from the consumer’s bank, to allow the FinTech company to initiate transactions in the customer’s checking account. Access to the consumer’s checking account was typically arranged through a third party service such as Plaid or Yodlee which provides lenders with consumer transaction history, balance data, income history, identify verification and account authentication. Access to this data was often critical to the functioning of the business and any restriction on the availability of this data would materially harm the business of the FinTech company in question.
  • In many cases, FinTech companies engaged in lending were using so-called “alternative” credit data. This term usually means financial and personal data other than standard FICO-type scores based on historical debt repayment experience.140 Use of this type of data is governed by a variety of federal and state statutes, most notably the Fair Credit Reporting Act (FCRA) and the Equal Credit Opportunity Act (ECOA.)141 None of the companies interviewed saw these regulations as unduly restricting their preferred business practices, although all appeared to believe that increased access to new types of alternative data would improve their marketing and credit outcomes.
  • Regulatory complexity and compliance costs were a concern for almost all companies. In particular, companies noted the costs associated with running national digital businesses while complying with widely varying state law mandates.

Recommendations

The FinTech alternatives to STSDC described in this paper are here today and already helping some low-income working families make better financial choices. But the impact of these innovative solutions on an old and difficult problem will be limited unless they can be deployed more broadly. Only then will the benefits for low-income consumers, employers and, indirectly, governments, be felt. The risk of delay is real. Like most startup technology companies, FinTech companies have a relatively short window to achieve scale and demonstrate a clear path to profitability. If they fail to connect with enough customers, the power of their innovations to change a broken STSDC system will be lost.

With this in mind, we are recommending a number of specific actions private and public sector employers, employee benefit intermediaries, non-profits, and FinTech and other financial companies can and should take immediately to encourage use of FinTech alternatives to STSDC.

In addition, we are making optional recommendations for future state and federal legislative and regulatory action to facilitate the development of FinTech alternatives to STSDC. These actions would be very helpful in the long run, but are not necessary for the immediate benefits of FinTech alternatives to become apparent.

  • Employers Should Adopt and Subsidize Employee Financial Health Benefit Plans. Private sector companies with large numbers of low-income working families in their employee base should adopt and promote Employee Financial Health Benefit Plans (which can be incorporated into existing retirement-focused plans as a technical matter) that include the highest Utility products from companies in the Relevant FinTech Categories. The goal of each employer should be to reduce financial stress and encourage financially healthy behaviors and thereby improve employee satisfaction, reduce turnover and absenteeism. Measurement of “hard” financial results and “soft” behavioral results will critical to justifying these benefits, and companies should use behavioral/psychological cues to maximize participation.
    • At minimum, every Employee Financial Health Benefit Plan should include Income/Expense Variability Management Solutions.
    • Companies should also seek to include the product capabilities of companies in the Digital Financial & Cash Flow Management Software Solutions and Digital Savings Solution category in every Employee Financial Health Benefit Plan, so that the long-term benefits of better personal financial management are made available to employees, as well as the immediate benefits of liquidity support. To the extent possible, employers should make use of those products mandatory for those using other lending, savings, and income/expense management products in the Employee Financial Health Benefit Plan.
    • The employer/sponsor should subsidize employee fees if necessary.
    • Companies should make links to the highest Utility third party Digital Credit Access/Cost Improvement Lender products and Digital Credit Builder Lenders & Services products, as well as links to financial advice sites, available to employees as part of its Employee Financial Health Benefit Plan package, but should not otherwise need to subsidize or otherwise encourage usage of these products.
  • Employee Benefits Intermediaries Should Support Adoption of Plans. Intermediaries in the employee benefit business—such as benefits consultants, insurance providers, benefit managers, recordkeeping providers, time & attendance systems providers– should include Financial Health Benefit Plans in their core offerings to corporate clients to reduce the integration burden from individualized adoption. This is in the intermediaries’ business and financial interest as the addition of new benefits will require their services. Intermediaries should strengthen the financial case for Financial Health Benefits Plans by commissioning additional independent research studies analyzing the “hard” and “soft” costs and benefits of the adoption of these plans by employers.
  • FinTech Companies Should Broaden their Offerings. To provide improved Utility for their products, all companies in the Relevant FinTech Categories should consider incorporating the product capabilities of companies in the Digital Financial & Cash Flow Management Software Solutions and Digital Savings Solutions categories into their own product offering. This will help consumers better control their finances and become able to manage through Key Precipitating Factors for STSDC Use without borrowing.
  • Non FinTech Financial Companies Should Adopt FinTech Products. Banks, credit unions and other traditional providers of financial services should also consider incorporating the product capabilities of companies in the Relevant FinTech Categories into consumer offerings to help improve their own customers’ financial health.
  • FinTechs and Financial Sector Should Resolve Data Governance Issues. Given the low likelihood that the CFPB will issue final regulatory guidance under Section 1033 of Dodd-Frank Act, or that Congress will enact legislation that would resolve the ongoing controversy over access by FinTech companies (acting on behalf of their customers) to customer financial records and the use of consumer-granted transaction authority at banks, securities firms and asset managers,142financial services providers and FinTech companies must work together collaboratively to resolve the core questions of bank customer data sharing, consumer control and security. If these issues are left unresolved, they will create major business risks for both banks and FinTech companies. The best place to start negotiations, in our view, are the financial data principles proposed by CFSI in October of 2016:

 

“An inclusive and secure financial data ecosystem is one in which financial institutions, data aggregators and third-party application providers coordinate to provide data to consumers that are:

  • Available: Consumers have the ability to view their financial information within the trusted and secure third-party application of their choice. (“Availability”)
  • Reliable: Consumer financial data are timely, consistent, accurate and complete. (“Reliability”)
  • User-permissioned: Consumers provide explicit consent for access to and use of their data. Consumers can easily view, modify and revoke consent for data sharing. (“Consent”)
  • Secure: All entities follow applicable laws and industry best practices with regard to data privacy and security. (“Security”)
  • Limited to the application functionality: Only the minimum amount of data required for application functionality are collected, and the data are stored for the minimum amount of time needed. (“Minimization”)”143
  • The Non-profit Sector Should Advocate for FinTech Benefits and Data Governance and Consider Subsidizing Test Cases. All nonprofits should provide Employee Financial Health Benefits incorporating FinTech solutions to their own employees to gather data and provide a demonstration of the financial health benefits of this approach. Nonprofits focused on low-income working families should commission research and conduct advocacy for adoption of such Employee Financial Health Benefit plans by employers, and support industry efforts to resolve consumer data governance issues. Where appropriate, non-profits should consider subsidizing “demonstration case” adoption of Employee Financial Health Benefit Plans by other non-profit entities.
  • Public Sector Employers Should Adopt Plans Too. Federal and State governments should make Financial Health Benefit plans incorporating FinTech solutions available to all State and Federal Employees, including soldiers and civilian military employees.
  • Public Sector Legislative/Regulatory Actions Can Also Help.
    • Congress should make employer contributions/subsidization with respect to Employee Financial Health Benefit Plans tax deductible, at least to the extent of usage by low-income working families.
    • State regulators should work collaboratively to reduce the burden of 50-state licensing and compliance on FinTech companies, through coordinated licensing applications, collaborative examinations and similar efforts. 144
    • We believe that the Office of the Comptroller of the Currency’s (OCC) recent efforts to follow a special Federal “FinTech Charter” 145 approach to licensing uninsured digital national banks, while probably legally defensible, is wrong-headed policy, not clearly supported by other federal regulators,146 aggressively opposed by state regulators, 147 likely to be overturned by Congress if enacted in any case148 and should be replaced by a different and more useful approach. Instead of seeking to evade the issue, federal and state regulators (the OCC, Federal Deposit Insurance Corporation (FDIC), the Board of Governors of the Federal Reserve System and state banking regulators), with assistance from Congress as necessary, should resolve, once and for all, the question of whether insured banking charters (national and state) can be issued to FinTech companies with business models involving innovative digital deposit taking and other digital banking/lending activities that are (i) consistent with the purposes of banks generally but are (ii) inconsistent with the community banking format of locally-based customers and physical distribution coupled with a traditional mix of bank balance sheet and revenue components. It would be highly beneficial for some FinTech companies to become regulated banks, but only if they are regulated, and hold capital, in accordance with the characteristics and risks found in their own business and not based on an inapplicable “platonic ideal” of a traditional community bank. The current stalemate in de novo bank application processing for FinTech-focused banks, reportedly driven by the FDIC’s concerns about risk, the Federal Reserve’s issues with ownership concentration, and concerns about the historical performance of non-traditional banks, is hurting innovation and stifling competition that would benefit low-income working families and other consumers.
    • Use of “alternative data” by FinTech companies and others should be encouraged as a matter of general policy by the relevant state and federal agencies but continued to be regulated closely to prevent prohibited discrimination. Regulatory and statutory uncertainty about permitted uses of alternative data should be resolved to avoid unnecessarily restricting the provision of high Utility FinTech products to low-income working families.

 

Author’s Note:

I would like to thank the following people for their assistance in reviewing drafts of this paper and otherwise providing helpful suggestions and guidance: Howell Jackson, Douglas Simons, William Longbrake, Cantwell F. Muckenfuss, III, Morgan McVicar, Diane Baker, Nick Bourke, Corey Stone, John Haigh, Konrad Alt, Robert Glauber, Jim Redmond, JoAnn Barefoot and Richard Zeckhauser.

I want to give particular thanks to Snigdha Kumar, who acted as my primary research assistant and partner in this endeavor, and without whom it would not have been possible. I would also like to than Raushanah Bowdre, who provided valuable research assistance to the project.

Special Thanks to Scott Leland, Susan Gill and the rest of the very supportive staff at the Mossavar-Rahmani Center for Business & Government at Harvard Kennedy School.

And finally, my sincere thanks to the many FinTech executives who contributed their valuable time and energy to our interview process.

End Notes

1 Average family income, by group, 1947–2010 (2011 dollars) EPI.org [nd] Available at:

Individual wealth has become even more concentrated, with 77.2% of total wealth concentrated in the top 10% and 22% of total wealth concentrated in the top 0.1% as of 2012. Saez, E. and Zucman, G. Wealth Inequality in the United States Since 1913: Evidence from Capitalized Income Tax Data. National Bureau of Economic Research. 2014. P. 22. Available from:

2 Employed Full Time: Median usual weekly real earnings: Wage and salary workers: 16 years and over. FRED® Economic Data: Federal Reserve Board of St. Louis Economic Research. 2016. Available at:

There are useful critiques of the data techniques used in standard analysis of wage levels which suggest that real wages may have increased more than suggested and that consumption in the lower end of the income distribution has increased at a higher rate than real wages. See Sacerdote, Bruce. Fifty Years of Growth in American Consumption, Income and Wages. National Bureau of Economic Research, 2017

3 Cocco, Federica, Most US Manufacturing Jobs Lost to Technology, Not Trade, Financial
Times, December 2, 2016 /> 95d1533d9a62

4 Dunn, M. and Walker, J., Union Membership In The United States. U.S. Bureau of Labor
Statistics: Spotlight on Labor Statistics. P. 2. 2016.Available at:

5 Florida, Richard. “The Incredible Rise of Urban Real Estate.” The Atlantic: Citylab. 2016. Available at:

6 Schanzenbach, D.W., Nunn, R., Bauer, L. et. al. Where Does All the Money Go: Shifts in Household Spending Over the Past 30 Years. The Hamilton Project. [Brookings Institute] [nd] [table 1] Available at:

7 Siebens, Julie. Extended Measures of Well-Being: Living Conditions in the United States: 2011. US Census Bureau: Household Economic Studies. 2013. [figure 4] Available at:

8 Consumer and Community Development Research Section of the Federal Reserve Board’s Division of Consumer and Community Affairs. Report on the Economic Well-Being of U.S. Households in 2015. Board of Governors of the Federal Reserve. 2016. P. 18. Available at:

9 Farrell, D. and Greig, F. Weathering Volatility: Big Data on the Financial Ups and Downs of U.S. Individuals. JPMorgan Chase Institute. 2015. p. 3. Available at:

10 ”How financial stress can harm your health.” Foxnews.com. 2/4/15. Available at:

11 Maron, Dina Fine. “Poor Choices: Financial Worries Can Impair One’s Ability to Make Sound Decisions.” Scientific American. 8/29/13. Available at:

Ariely, Dan. “Poverty makes financial decisions harder. Behavioral economics can help.” PBS Newshour. 1/20/16. Available at:

Thompson, Derek. “Your Brain on Poverty: Why Poor People Seem to Make Bad Decisions.” The Atlantic. 11/22/13. Available at:

12Robertson, Linda. Moving the Needle: Raising Your Employees Financial Well-being to Lower their Financial Stress. 34th Annual ISCEBS Employees Benefit Symposium. [nd] Available at: eedle_Raising_Your_Employees_Financial_Wellbeing_to_Lower_Their_Financial_Stress.pdf#se arch=financial%20stress

Brune, Katherine W. Single Employer Case Study: Suntrust Bank. International Foundation of Employment Benefit Plans. [nd] Available at: ess

13 The reasons for the emergence of these new types of STSDC products are complex and beyond the scope of this paper. However, it is worth noting at least one of the key reasons— the rise of automated credit scoring, which replaced older underwriting methods used to lend to low-income consumers with a numerical score reflecting historical credit usage and payment history. Credit scoring allowed significantly higher levels of credit risk differentiation among borrowers, which in turn led to a much greater range of risk-based interest rates that could be charged for loans. Credit scores also left many potential borrowers—those with poor or no credit scores–unable to borrow from traditional banks and finance companies. STSDC products, which do not require a credit score, were a natural outgrowth of this development.

14 During that period the U.S. federal government also sought to address underlying economic pressures on low-income families through the Affordable Care Act, increased Medicaid eligibility, Increased low-income tax credits, extended unemployment benefits and minimum wage and overtime reform. Goldfarb, Zachary A., “Obama to raise minimum wage for government contract workers.” The Washington Post. 1/28/14. Available at:

Holan, Angie Drobnic. “A credit for workers cuts taxes for middle class.” Politifact. 9/18/08. Available at:

Onink, Troy. “You Can Get $10,000 Per Child In College Tax Credits, Thanks To The Fiscal Cliff Deal.” Forbes.com. 1/16/13. Available at:

Dovere, Edward-Isaac and Levine, Marianne. “Obama overtime rule could raise wages for 5 million.” Politico. 6/29/15. Available at:

15 Section 733 of the proposed Financial Choice Act of 2017, championed by House Financial Services Committee Chairman Jeb Hensarling, contains a provision removing the CFPB’s “rulemaking, enforcement or other authority with regard to payday loans, vehicle title loans or other similar loans.”

16 Marte, Jonnelle. “There is a fierce tug of war over the future of the CFPB.”
Washingtonpost.com: Get There. 1/27/17]. Available at:

17 FinTech generally refers to technology-driven replacements for traditional financial services products and services. These range from “blockchain” solutions for bank and asset manager back offices to digital small business lending and a large variety of digital consumer financial products. FinTech providers seek to offer consumers and businesses faster, less expensive, easier to use and more satisfying ways to access needed financial services. Investment in FinTech globally has been growing at a hectic pace, reaching $5.3 billion in the first quarter of 2016 alone, a 67 percent increase over the same period of the prior year. PWC estimates that cumulative investment could exceed $150 billion in the next three years. PwC. Blurred lines: How FinTech is shaping Financial Services. 2016. Available at:

18 The Pulse of Fintech – Q3 2016: Global Analysis of Fintech Venture Funding. KPMG/CBInsights. 11/16/16. p. 29. Available at:

19 Roberts, B, Povich, D. and Mather, M. Low-Income working Families: The Growing Economic Gap. The Working Poor Families Project. 2012-2013. p. 9. Available at:

20 Ibid. “The poverty line is adjusted annually for inflation and takes into account the number of people in a family: The larger the family size, the higher the poverty line. In 2010, the poverty line for a nonfarm family of four (two adults, two children) was $22,213. A four-person family earning even one more dollar than $22,213 in 2010 was not officially poor, even though its “extra” income hardly lifted it out of dire economic straits. Poverty experts have calculated a no-frills budget that enables a family to meet its basic needs in food, clothing, shelter, and so forth; this budget is about twice the poverty line. Families with incomes between the poverty line and twice the poverty line (or twice poverty) are barely making ends meet, but they are not considered officially poor. When we talk here about the poverty level, then, keep in mind that we are talking only about official poverty and that there are many families and individuals living in near poverty who have trouble meeting their basic needs, especially when they face unusually high medical expenses, motor vehicle expenses, or the like. For this reason, many analysts think families need incomes twice as high as the federal poverty level just to get by (Wright, Chau, & Aratani, 2011).Wright, V. R., Chau, M., & Aratani, Y. (2011). Who are America’s poor children? The official story. New York, NY: National Center for Children in Poverty. They thus use twice-poverty data (i.e., family incomes below twice the poverty line) to provide a more accurate understanding of how many Americans face serious financial difficulties, even if they are not living in official poverty.”

We note that all income-based poverty definitions are affected by the difference between “spending poverty” and “income poverty.” Morduch, Jonathan and Schneider, Rachel. The Financial Diaries—How American Families Cope in a World of Uncertainty. Princeton University Press. 2017. PP. 163-165.

21 Ibid.

22 Morduch, Jonathan and Schneider, Rachel. “Mismatch: How Income and Expense Volatility Are Undermining Households.” Stanford Social Innovation Review. 1/12/16. Available at: ng_households

23 Farrell, D. and Greig, F. Weathering Volatility. p. 3.

24 Farrell, D. and Greig, F. Coping with Costs: Big Data on Expense Volatility and Medical Payment.s JPMorgan Chase Institute. 2017. Available at:

25 Aspen Institute. Income Volatility: a Primer. Aspen Institute’s Financial Security Program: Expanding Prosperity Impact Collaborative (EPIC). 2016. Available at:

26 Farrell, D. and Greig, F. Weathering Volatility. p. 3.

27 Ibid.

28 Consumer and Community Development Research Section of the Federal Reserve Board’s Division of Consumer and Community Affairs. Report on the Economic Well-Being of U.S. Households in 2015. p. 18.

29 This software allows employers to fit work schedules to demand far more aggressively than in the past and can produce significant variance in paycheck amounts. Mitchell, David S. Stable and Predictable Scheduling as Antidote to Income Volatility. Aspen Institute’s Financial Security Program: Expanding Prosperity Impact Collaborative (EPIC). 2017. p. 3. Available at:

30 Aspen Institute. Income Volatility: a Primer. p. 6.

31 Farrell, D. and Greig, F. Weathering Volatility. p. 8.

32 Consumer and Community Development Research Section of the Federal Reserve Board’s Division of Consumer and Community Affairs. Report on the Economic Well-Being of U.S. Households in 2015. p. 25.

33 Ibid. p. 23.

34 Rhee, Nari and Boive, Illana, The Continuing Retirement Savings Crisis, National Institute on Retirement Security, March 2015, p.8

35 Lin, JT, Bumcrot, C., Ulicny, T, et. al. Financial Capability in the United States: 2016. FINRA Investor Education Foundation. 2016. Available at: National Financial Capability Study

36 Rhee, Nari and Boive, Illana, The Continuing Retirement Savings Crisis, National Institute on Retirement Security, March 2015, p.10

37 Consumer and Community Development Research Section of the Federal Reserve Board’s Division of Consumer and Community Affairs. Report on the Economic Well-Being of U.S. Households in 2014. Board of Governors of the Federal Reserve. 2014. pp. 2-3. Available at:

38 This assumption is generally consistent with research results showing average reductions of between 11% and 17% of total monthly spending in reduced income months. Morduch, J., and Schneider, R. The Financial Diaries.

39 Levy, R. and Sledge, J. A Complex Portrait: An Examination of Small-Dollar Credit Consumers. Center for Financial Services Innovation. 2012. p. 6. Available at:

40 Pew Safe Small-Dollar Loans Research Project. Payday Lending in America: Who Borrows, Where They Borrow, and Why. The Pew Charitable Trusts. 2012. pp. 14-18. Available at: eportpdf.pdf

41 There is a very strongly held view among some experts in this area that longer-term installment lending is always the best alternative to STSDC. This view is based in part on the idea that short-term loans with lump sum repayments, typical of STSDC are inherently unaffordable and that borrowing on a longer-term basis with graduated and more affordable terms is always better. The arguments for this view seem compelling. The typical payday loan can take more than one-third of a borrower’s next paycheck and auto-title loans and deposit

42 “The Cash Advance Loans Industry – Overview.” Cashadvanceonline.net. [nd] Available at:

“A Short History of Payday Lending Law.” The Pew Trusts. 2012. Available at:

43 About the Payday Advance Industry. Community Financial Services Association of America. [nd] Available at:

44 Other independent estimates of payday loan users are lower—The Pew Charitable Trusts a estimate, based on actual unique borrowers in a year (i.e., not counting rollovers or repeated borrowings) is closer to 12 million.

“Payday Loan Facts and the CFPB’s Impact” The Pew Trusts. 2016. Available at:

45 Ibid.

46 “What is a payday loan?” Consumer Financial Protection Bureau. [nd] Available at:

47 Flanner, Mark, and Samolyk, Katherine. “Scale Economies and Payday Loan Stores.”
Proceedings of the Federal Reserve Bank of Chicago’s 43rd Annual Conference on Bank Structure and Competitiveness. (May 2017) pp.233-259. The high level of charge-offs is often masked by industry-provided statistics that show low levels of defaults (e.g., 5%) on individual payday loans. But when default rates are considered cumulatively for multi-loan rollovers the losses can be ten times that rate.

48 Payday Loans and Deposit Advance Products: A White Paper of Initial Data Findings.

Consumer Financial Protection Bureau. 2013. p. 27. Available at:

49 Douglas, Danielle, “Wells Fargo, U.S. Bank to end deposit advance loans, citing tougher regulation. Washington Post. January 17, 2014. Available at:

50 Bank overdraft protection has traditionally not been considered a “loan” under the Truth in Lending Act, 15 USC 1601 et seq. (TILA) and its implementing Federal Reserve Regulation Z, 12 CFR Part 226

51 Consumer Banking Project. Heavy Overdrafters: A Financial Profile. The Pew Charitable Trusts. 2016. Available at:

52 Bakker, T, Kelly, N., Leary, J. et. al. Data Point: Checking Account Overdraft. Consumer Financial Protection Bureau. 2014. p. 5. Available at:

53 Consumer Banking Project. Heavy Overdrafters: A Financial Profile.

54 Sullivan, Bob. “1 in 10 Millennials Overdraft More Than 10 Times a Year.” Credit.com. 1/22/15. Available at:

55 How a Set of Small Banks Compares on Overdraft: An analysis of programs, fees, and terms at 45 financial institutions. The Pew Charitable Trusts. 2016. p. 6. Available at:

56 Andriotis and Rudegeair. “Banks Feel Pinch From Declining Overdraft Fees” The Wall Street Journal. 6/16/15. Available at:

57 Small-dollar Loans Project. Auto Title Loans: Market practices and borrowers’ experiences.
The Pew Charitable Trusts. 2015. pp. 3-4. Available at:

Consumer Financial Protection Bureau. CFPB Finds One-in-Five Auto Title Loan Borrowers Have Vehicle Seized for Failing to Repay Debt. 2016. Available at:

58 National Pawnbrokers Association. Pawn Industry Statistics. [2016.] Available at:

National Pawnbrokers Association. “Pawnbrokers Report Trends & Steady Industry Growth.” Pawn Shops Today. [2015.] Available at: Available at:

59 First Cash Express, a publicly traded STSDC company, has a market capitalization of around $2.5 billion and an ROTCE of around 25% as of the date of this paper. See:

60 CFSI’s study differentiated between what it described as “STC” or short-term credit and “VSTC” or very-short term credit. VSTC, as defined, is probably the closest analogue to the definition of STSDC used in our study (although it excludes auto title lending because some auto title loans have longer terms), but we have for purposes of this discussion treated both STC and VSTC as equivalents to STSDC unless the context requires otherwise.

61 Levy, R. and Sledge, J. p. 12.

For a slightly different take, see: McKernan, S.M., Ratcliffe, C., and Quackenbush, C. Small Dollar Credit: Consumer Needs and Challenges. Urban Institute. 2014. Available at:

62 Levy, R. and Sledge, J. p. 6.

63 Ibid.

64 The number of respondents indicating that they use STSDC to compensate for Negative Cash Flow—or to put it more bluntly, personal insolvency–is particularly troubling as the use of any credit in this circumstance is likely, as CFSI notes, to result in a “debt trap” from which there is little chance of escape.

65 Levy, R. and Sledge, J. p. 6.

66 Keiler, Ashlee. Abusive Lending Practices Can Lead to Negative Long-term Consequences for Borrowers, Communities. Consumerists.com. 2015. Available at:

67 Avery, Robert B., Samolyk Katherine A. Payday Loans versus Pawn Shops: The Effects of Loan Fee Limits on Household Use. Board of Governors of the Federal Reserve System/The Consumer Financial Protection Bureau. 10/9/11. P. 6. Available at:

68 Payday, Vehicle Title, and Certain High-Cost Installment Loans (proposed June 2, 2015) Consumer Financial Protection Bureau. p. 173. Available at: h-Cost_Installment_Loans.pdf .

69 Wolff, Sarah D. The Cumulative Costs of Predatory Practices: The State of Lending in America & its Impact on U.S. Households. Center for Responsible Lending. 2015. p. 29. Available at:

Melzer, Brian T. “The Real Costs of Credit Access: Evidence from the Payday Lending Market,” Quarterly Journal of Economics. February 2011. (2011) 126 (1): 517-555. Available at:

Sherter, Alain. “1,000% Loans? Millions of Borrowers face crushing costs.” Moneywatch. CBS News. 2016. Available at:

70 Trial, Error, and Success in Colorado’s Payday Lending Reforms. Pew Charitable Trusts. 2014. Available at: 4.pdf

71 One in seven job seekers with blemished credit has been passed over for employment after a credit check. Traub, Amy. Discredited—how employment credit checks keep qualified workers out of a job, Demos (n.d.)

72 Siebens, Julie. Extended Measures of Well-Being: Living Conditions in the United States: 2011.
Household Economic Studies, U.S. Census Bureau. 2013. Pp. 7-8. Available at:

73 Mendenhall, R., Edin, K. and Crowley, S. et. al. “The Role of Earned Income Tax Credit in the Budgets of Low-income Families.” Social Service Review. 2012. pp. 10-11. Available at:

74 Policy makers have from time to time discussed ways to spread out EITC payments over time as a potential substitute for STSDC. Jagoda, Naomi, “Dem Senator Urges Expansion of Tax Credit for Low-income Workers.” TheHill.com. 06/07/16. Available at:

75 Wood, Michael. “Payday Lending Cost the Economy 14,000 Jobs in 2011” National Association of Consumer Advocates. 2013. Available at:

76 Kurban, H., Diagne, A.F. and Otabor, C. Economic Impact of Payday Lending in Economically Vulnerable Communities: Alabama, Florida, Louisiana and Mississippi. Howard University Center on Race and Wealth. 2014. p. 31. Available at:

77 Tringali, Victor M. and Giandonato, Joseph, “Views: 5 ways a financial health component can boost a wellness plan.” Employee Benefit Advisor. 6/20/16. Available at:

Garman, E.T., Leech, I.E., and Grable, J.E. The Negative Impact Of Employee Poor Personal Financial Behaviors On Employers. Association for Financial Counseling and Planning Education. 1996. Pp. 164-165. Available at:

Meyer, Cynthia. “The ROI of Workplace Financial Wellness.” Financialfinesse.com. 2016. Available at:

78 There is much to debate. For example, Mullainathan and Eldar argue that the cumulative impact of economic stress on cognitive capacity and specifically on people’s need to focus on the emergency at hand, rather than the bigger picture (“tunneling”) leads to poor decisions on things like STSDC. Mullainathan, Sendhil, and Eldar Shafir. 2013. Scarcity: why having too little means so much.

79 Shapiro, Robert. The Consumer and Social Welfare Benefits and Costs of Payday Loans: A Review of the Evidence. Sonecon. 2011. p. 15. Available at:

80 “Harris Poll: Payday Loans and the Borrower Experience.” Community Financial Services Association of America. [nd] Available at:

81 Summers, Adam B. Payday Lending: Protecting or Harming Consumers? Reason Foundation. 2013. Available at: /> See

Ronald Mann has also published numerous articles which are cast doubt on some of the factual assumptions used to justify regulatory action. Mann, Ronald. “Assessing the Optimism of Payday Loan Borrowers.” Supreme Court Economic Review 21, no. 1 (2014)

Available at:

82 Elliehausen, Gregory. An Analysis of Consumers’ Use of Payday Loans. The George Washington University School of Business. 2009. pp. 60 – 63. Available at:

83 DeYoung, Mann, Morgan, et. al. [sic] “The Myths of Payday Lending.” Foundation for Economic Education. 10/21/15. Available at:

84 “The Unbanking of America,: Book Release and Talk with Professor Servon.” Inside Penn Wharton PPI. 1/30/17. Available at:

85Bhutta, N., Goldin, J., and Homondoff, T. “Consumer Borrowing after Payday Loan Bans.” The Journal of Law and Economics, 59(1): 225-259. 2/16. Available at:

86 Olen, Helaine. “The Quest to Improve America’s Financial Literacy Is Both a Failure and a Sham.” Pacific Standard. 1/7/14. Available at:

87 How State Rate Limits Affect Payday Loan Prices. The Pew Charitable Trusts. 2014. Available at:

88 State Payday Regulation. In 2014, the Pew Charitable Trusts published an analysis that classified state legal regulation of payday lenders into three categories, “Restrictive,” “Hybrid,” and “Permissive:”

89 Division of Banks, Consumer Affairs and Business Regulation. “Payday Loans” Massachusetts Office of Consumer Affairs & Business Regulation. 2017. Available at:

Thompson, Connie. “State regulators: Most online payday lenders illegal” Komonews.com. 1/12/15. Available at:

90 State Auto Title Lending Regulation. A, a 2012 study by the Consumer Federation of America (CFA) described the regulatory landscape for auto title loans in a similar fashion:

“State laws determine whether car-title loans are authorized, the terms of the loans, and consumer protections for the loan or repossession of the vehicle if a borrower cannot repay in full when the loan is due. CFA categorizes the legal status of car-title lending, depending on whether this loan product is specifically authorized, whether title lending operates through legal loopholes, or are effectively prohibited.

Permit: 17 states permit car-title loans at triple-digit APRs: Alabama, Arizona, Delaware, Georgia, Idaho, Illinois, Mississippi, Missouri, Nevada, New Hampshire, New Mexico, South Dakota, Tennessee, Texas, Utah, Virginia, and Wisconsin. Of these states, Delaware, Idaho, Illinois, Missouri, Nevada, New Mexico, South Dakota, Utah, and Wisconsin set no cap on the cost of a car-title loan.

Loophole: Car-title lenders operate in four additional states by structuring loans to fall outside of state credit law definitions. For example, title loans are available in California and South Carolina for larger amounts to avoid the small loan rate cap. Kansas title loans are structured as open-end credit, since Kansas has no rate cap for open-end credit from licensed lenders. In Louisiana, car-title lenders make loans for more than $350 and terms exceeding two months to avoid state law restrictions.

Restrict: The remaining states authorize car-title loans at lower rates and likely do not have car-title lenders operating in the state, prohibit by statute or do not permit car-title lending under existing credit laws.”

Consumer Federation of America. Car Title Loans Regulation. 2012. Available at:

91 These efforts include distributing research results, white papers and personal advocacy in state legislatures and with federal elected and regulatory officials, in a manner reminiscent of the very successful efforts of the Russell Sage Foundation to reform consumer installment lending practices in the early decades of the 20th Century. See Trumbull, Gunnar. Consumer Credit in France and America—Credit and Welfare. Cambridge University Press, 2014.

92 Volz, Matt. “Montana Voters Approve Payday Loan, Real Estate Tax Initiatives.” Missoulian. 11/3/10. Available at:

Farmer, Liz. “Facing 62% Interest Rates, South Dakota Voters Regulate Payday Lending.”

Governing: The States and Localities. 11/9/16. Available at:

93 Consumer Financial Protection Bureau. CFPB Report Finds Loopholes In Military Lending Act Rules Rack Up Costs For Servicemembers. 2014 Available at:

94 In 2016, the CFPB, using newly granted authority under the Dodd-Frank Act, proposed regulations to prohibit certain practices by payday lenders, car title lenders, bank providers of deposit advances and providers of high-rate installment loans. See Rosenblum, Jeremy T. CFPB issues proposed payday/auto title/high-rate installment loan rule. Consumer financial services group at Ballard Sphar: CFPB Monitor. 2016. Available at: rule/

95 Consumer Financial Protection Bureau. CFPB Study of Overdraft Programs: A white paper of initial data findings. 2013. Available at:

Bomey, Nathan. “Agency: TCF National Bank tricked customers on overdraft fees.” USAtoday.com. 1/19/17. Available at:

96 The Pew Trusts argue forcefully that only “percentage of income” repayment limits are effective in curbing payday loan abuse—a view not reflected in the CFPB proposal. Bourke, Nick. Proposed Payday Lending Rule Would Leave Borrowers Vulnerable. Pew Charitable Trusts: Small Dollar Loans. 2016. Available at:

97 Regulation E (12 CFR 1005) is the consumer protection regulation that implements the Electronic Fund Transfer Act of 1978 (EFTA) (15 USC 1693). Regulation E provides a basic framework that establishes the rights, liabilities, and responsibilities of participants in electronic fund transfer systems such as automated teller machine transfers, telephone bill-payment services, point-of-sale (POS) terminal transfers in stores, and preauthorized transfers from or to a consumer’s account (such as direct deposit and social security payments).

98 (74 Fed. Reg. 59033 (November 17, 2009) and 75 Fed. Reg. 31665 (June 4, 2010)).

99 Bourke, Nick. “Most Large Banks Fail to Meet Overdraft Best Practices.” Pew Charitable Trusts: Consumer Banking. 2016. Available at: room/press-releases/2016/12/20/most-large-banks-fail-to-meet-overdraft-best-practices

100 Federal Deposit Insurance Corporation. 6714-01-P Guidance on Supervisory Concerns and Expectations Regarding Deposit Advance Products. 2013. Available at:

“Federal regulators are …including a “cooling-off period” that prevents borrowers from taking more than one deposit advance during a monthly pay cycle….Another key concern is that banks determine a customer’s ability to repay before making a loan, a standard underwriting practice in all other types of lending. Regulators recommended reviewing at least six months of customer’s banking activities. If a customer’s account is routinely overdrawn, banks should hold off on extending credit, the agencies say.”

Douglas, Danielle. “Regulators put tougher restrictions on bank payday loans” Washington Post. 11/21/13. Available at:

101 Douglas, Danielle, “Wells Fargo, U.S. Bank to end deposit advance loans, citing tougher regulation.

102 Bhutta, N., Goldin, J., and Homondoff, T. “Consumer Borrowing after Payday Loan Bans.” The Journal of Law and Economics, 59(1): 225-259. 2/16. Available at:

103 “ABA Urges CFPB to Protect Small-Dollar Loans by Banks” ABA Banking Journal. 10/10/16. Available at:

104 Reiling, David. “A -in -small -dollar -lending” Ford Foundation. 4/20/16. Available at:/

Foster-Keddie, Kevin. “CFPB Plan Will Halt Traditional Lenders’ Small-Dollar Loan Progress.” American Banker. 6/16/16. Available at:

Hayashi, Yuka. “CFPB Presses Banks, Credit Unions to Offer ‘Small-Dollar Loans’” The Wall Street Journal. 2/9/16. Available at:

105 Andriotis, AnnaMaria and Rudegeair, Peter. “Banks Feel Pinch From Declining Overdraft Fees.”

The author of this paper has suggested that banks would be better off reforming their overdraft practices in exchange for relief from the impact of the “Durbin” interchange fee restrictions included in the Dodd-Frank Act. Baker, Todd. “How to Solve the Bank Fee Conundrum Hurting Consumers. American Banker. 2/17/17. Available at:

106 “Thin File” Investopedia.[nd] Available at:

107 Rotter, Kimberly. “Guide: How to fix your credit.” Creditsesame.com. 2016. Available at:

108 For reasons of scope, we also did not consider the potential for insurance-based alternatives to STSDC—an area with significant potential but, to our knowledge, no companies actively selling products as of the time of our research.

109 The author has, since March of 2017, served as a Senior Advisor to Aspiration Partners LLC, the operator of one of the Alt Consumer Banks on the list. Aspiration was not interviewed by the author in connection with this paper.

110 Morduch and Schneider. p. 119

111 Traub, Amy.

112 In the author’s view, non-borrowing Utility solutions to the Key Precipitating Factors for STSDC Usage are generally preferable to borrowing-based solutions. See Baker, Todd H. “High Cost Credit Better than No Credit? Not Necessarily.” American Banker. 5/31/16. Available at: However, as the purpose of this paper is to assess the marginal benefits of specific alternatives to STSDC in terms of Utility and Scalabilty, the inquiry is limited to those questions, leaving the larger question of when high-cost borrowing is inherently damaging to another time.

113 There are ongoing legal controversies relating to the use of banks for this purpose.

Olthoff, Mark A. “Recent Supreme Court Action Creates Uncertainty in Financial Industry: Madden v. Midland Funding” The National Law Review. 2016. Available at:

“Colorado Attorney General Pursues ‘True Lender’ and ‘Madden” Actions Against Major Non-Bank Online Lenders.” Pepper Hamilton, LLP. 4/3/17. Available at:

114 Because of this, the company in question is sometimes viewed as not a true “FinTech” company, although it uses technology heavily in its credit decisioning and continues to build out online and mobile capabilities.

115 “What is a Credit Score?” Myfico.com.[nd] Available at:

116 Weiss, Gregor N. F. and Pelger, Katharina and Horsch, Andreas, Mitigating Adverse Selection in P2P Lending – Empirical Evidence from Prosper.com (July 29, 2010). Available at
SSRN: or

117 See also Baker, Todd. “Marketplace Lenders are a Systemic Risk.” American Banker. 8/17/15. Available at:

118 Barba, Robert. “Why the JPM-Intuit partnership is a big step for data sharing.” American Banker. 1/25/17. Available at:

119 Yurcan, Bryan, “FinTech companies form lobbying group focused on data sharing.” American Banker. 1/19/17. Available at:

120 Bureau of Consumer Financial Protection Bureau. Request for Information Regarding Consumer Access to Financial Records. 2016. Available at: ing_Consumer_Access_to_Financial_Records.pdf

121 Manbeck. And Franson, M. The Regulation of Marketplace Lending: A Summary of the Principal Issues (2015 Update) Chapman & Cutler, LLP. 2015. pp. 16 & 18. Available at: tePaper040815.pdf

122 To provide a comparison, in 2000 total unsecured consumer finance receivables (except real estate and auto) held by non-bank finance companies were $ 94.2 billion. Dynan, K.E., Johnson, K.W. and Slowinski, S.M. Survey of Finance Companies, 2000. Federal Reserve Bulletin. 2002. Available at:

/> 123 United States Securities and Exchange Commission. Annual Report Pursuant to Section 13 ot 15(d) of the Securities Exchange Act of 1934. 2000. Available at: [Note 4 to Financial Statements]124 United States Securities and Exchange Commission. Amendment No. 6 to Forms S-1 Registration Statement Under The Securities Act of 1933: Elevate Credit, Inc. 2017. Available at:

125 By “standard” in this context, we mean prime or near-prime lenders using traditional credit scoring and charging lower market rates.

126

 

127 Due to the so-called Durbin Amendment contained in the Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank Act), Pub. L. No. 111-203, 124 Stat. 1376 (2010), which resulted in regulations that cap debit card interchange fees charged by banks with more than $10 billion in assets, these partnerships are generally entered into with smaller banks.

128 Deposits gathered by a bank in this way are generally considered “brokered deposits” for purposes of various bank regulations. 12 CFR Section 337.6. Among other restrictions, when a bank with under $10 billion in assets has more than a 10% ratio of brokered deposits to total assets, the bank’s deposit insurance premiums may rise substantially. Available

at:

Federal Deposit Insurance Corporation. Identifying, Accepting and Reporting Brokered Deposits: Frequently Asked Questions. 2016 Available at:

Brokered deposits also have negative consequences at larger (above $50 billion in assets) banks where they can significantly increase liquidity requirements and therefore reduce profitability.

Ireland, Oliver I. Liquidity Coverage Ratio: A Quick Reference. Morrison & Foerster. 2015. Available at:

 

129 Partington, R. and Khan, J. “Neobanks Chasing U.K.’s Biggest Lenders Face Battle for Survival” Bloomberg Technology. 9/28/16. Available at:

130 Taylor, Susan Johnston. “5 Employee Benefit Trends to Watch in 2016.” US News & World Report. 12/15/15. Available at: /> 131 Tringali, Victor M. and Giandonato, Joseph.

Garman, E.T., Leech, I.E., and Grable, J.E. Pp. 164-165.

132 “U.S. Largest Employers List” Statisticbrain.com. 2016. Available at:

133 Morduch and Schneider, pp. 96-109.

134 “The Importance of Emergency Savings.” Military.com. [nd] Available at:

135 See note 126.

136 Meyer, Cynthia.

137 CFPB Releases Tool to Help Measure Financial Well-Being. Consumer Financial Protection Bureau. 2015. Available at: /> Parker, Sarah. Eight Ways to Measure Financial Health. Center for Financial Services Innovation. 2016. Available at:

138 Baker, Todd H. “Reality Check for Marketplace Lenders.” American Banker. 3/15/16. Available at:

139 Baker, Todd. Lending Club Should Become a Bank as Fast as it Can. The Financial Times, FT Alphaville. November 25, 2016.

140 The CFPB describes such data as follows: “Alternative data includes sources such as timely payment of rent, utilities, or medical bills, as well as bank deposit records, and even internet searches or social media information—data that credit bureaus do not traditionally

consider. However, a consumer who lacks a credit history but who makes timely rent and utility payments may be as likely to repay a loan as another consumer with a higher credit score.”

Brody, M.H., Lilley, S. and Tsai, J.and Tsai, J. et. al. “CFPB Calls for Comment on Alternative Data.” Consumer Financial Services Review. 2017. Available at: /> Request for Information Regarding Use of Alternative Data and Modeling Techniques in the Credit Process. [Docket No. CFPB-2017-0005] Bureau of Consumer Financial Protection. 2017. Available at: /> 141 Bernstein, L.A., Earley, M.B., and Bons, P. “Request for Information Regarding Use of Alternative Data and Modeling Techniques in the Credit ProcessAlternative Data and Credit Scores: Will it Trigger CFPB Enforcement?” The Temple 10Q. 2017. Available at:

142 Popper, Nathaniel. “Banks and Tech Firms Battle Over Something Akin to Gold: Your Data.” The New York Times. 3/23/17. Available at:

143 “CFSI’s Consumer Data Sharing Principles: A Framework for Industry-Wide Collaboration” Center for Financial Services Innovation. 2016. Available at: p1

144 Clozel, Lalita. “California regulator acknowledges ‘legitimate criticisms’ of state licensing.” American Banker. 2/4/17. Available at:

145 Office of the Comptroller of the Currency. “OCC To Consider Fintech Charter Applications, Seeks Comment” U.S. Department of the Treasury. 2016. Available at:

146 Brainard, Lael. “Where Do Banks Fit in the Fintech Stack?.” Northwestern Kellogg Public-Private Interface Conference on “New Developments in Consumer Finance: Research & Practice.” 4/28/17. Available at: /> 147 This issue is currently being litigated by the Conference of State Bank Supervisors. Clozel, Lalita. “State Regulators Sue OCC Over FinTech Charter.” American Banker. 4/26/17. Available at: Clozel, Lalita. “Curry Defends Fintech Charter After State Regulators Lawsuit.” American Banker. 4/28/17. Available at:

148 Benjenk, Randy. “State Banking Regulators Oppose OCC Fintech Charter.” Cov Financial Services. 2017. Available at: See also Jeffries, Tara. OCC Chief Pushes Back on Criticisms of FinTech Charter Plan. Morning Consult. March 6, 2017.

 

Authors:

Todd H. Baker
Senior Fellow, Mossavar-Rahmani Center for Business & Government at Harvard Kennedy School

Report: Marketplace Lending Securitization Tracker Q1 2017

Report: Marketplace Lending Securitization Tracker  Q1 2017

Marketplace lending securitization remains a bright spot in the ABS market. Total issuance topped $3 Bn this quarter with cumulative issuance now totaling $18.0 Bn across 80 deals. The movement towards rated securitizations at larger transaction sizes continues. All deals were rated in this quarter, with record-sized consumer deals from SoFi, a large multi-seller deal […]

Report: Marketplace Lending Securitization Tracker  Q1 2017

Marketplace lending securitization remains a bright spot in the ABS market. Total issuance topped $3 Bn this quarter with cumulative issuance now totaling $18.0 Bn across 80 deals.

The movement towards rated securitizations at larger transaction sizes continues. All deals were rated in this quarter, with record-sized consumer deals from SoFi, a large multi-seller deal from Marlette, and the first prime paper deal from Lending Club. All deals this quarter had at least one rating.

New issuance spreads continued to tighten and flatten—a credit friendly environment for securitization. In 1Q2017, we saw spreads tighten in riskier tranches, indicating strong investor appetite for MPL ABS paper in the market.

Bank and non-bank platform partnerships continue to emerge.

Over 15 banks are purchasing loans from marketplace lenders.

Our year-end forecasts volumes for new issuance of $11.2 Bn remain on-track. New issuers and repeat issuers are increasing deal activity.

3rd party solutions are emerging to improve investor confidence.
Originators are relying on 3rd party solutions to address “stacking”, perform data verification, loan validation, and improve investor confidence.

We expect higher volatility from rising rates, regulatory uncertainty, and an exit from a period of unusually benign credit conditions. Platforms that can sustain low-cost stable capital access, build investor confidence via 3rd party tools, and embrace strong risk management frameworks will grow and acquire market share.

 

Authors

Wilfred Daye wilfred@peeriq.com

Jianguo Xiao jianguo@peeriq.com

Yishu Song yishu@peeriq.com

Investors should consider PeerIQ as only a single factor in making their investment decision. Please refer to the Disclosure Section, located at the end of this report, for information on disclaimers and disclosures.

 

Introduction

Benign Macro Conditions Amidst Rising Rates

On March 15th, the FOMC announced an increase in the Fed Funds rate with a new target of between 75 and 100 basis points. The decision comes at a time when the unemployment rate is at 4.7%, the US economy generated 2.1% GDP growth in 4Q2016, and robust year-over-year wage growth of 2.8%. Fed futures forecast two more rate hikes before end of year. Consumer confidence continues to climb to its highest levels since August 2001.

The “risk-on” environment has continued in the first quarter of 2017 as the global reach for yield intensifies. 10-year bond yields remain at 2.4%, while the equity markets have continued to rally with the S&P 500 up 5.8% YTD and other equity indices near record highs.

For 1Q2017, overall credit markets have exhibited low volatility and credit conditions remain relatively favorable. Implied volatility on 3-month CDX HY options reached another historical low of 34%. On-the-run CDX HY traded 16 basis points tighter than 2016 year-end close with a range bound between 307 to 355 basis points. Further, in the US CLO market, new-issue spreads continue to grind tighter and reach historical tights.

Strong Capital Markets Activity in Marketplace Lending ABS

The encouraging macro conditions continued to support the growth of the MPL ABS market after the holiday season. The first quarter of 2017 witnessed several record-sized MPL securitizations. Seven deals priced, totaling $3.0 Bn, comprised of consumer, student, and SME collateralized deals. New issuance volume doubled in 1Q2017 vis-à-vis 1Q2016; the cumulative issuance volume now stands at $18.0 Bn since the inception of MPL ABS market in 2013.

The industry continues to experience strong investor sentiment as evidenced by growing deal size and improved deal execution. SCLP2017-1 is not only SoFi’s largest deal, but is also the largest unsecured consumer ABS in MPL ABS history with a collateral pool of $650 Mn. Marlette Funding Trust 2017-1 is the largest deal to date for the MFT shelf, with a size of $333 Mn, pricing tighter than MFT 2016-1 across all tranches with a flattened credit curve. On the SME side, Kabbage’s $525 Mn deal, led by Guggenheim, was also oversubscribed.

Another milestone reached in this quarter was the Arcadia Receivables Credit Trust 2017-1, which was the first ABS deal backed by LendingClub Prime loans. This continues

LendingClub’s presence in the securitization market after last quarter’s initial deal from the LCIT LendingClub shelf.
In addition to securitization activity, several partnerships with non-bank lenders developed in this quarter. Deutsche Bank upsized its revolving credit facility with OnDeck to $214 Mn. Prosper announced a $5.0 Bn loan-buying deal with a consortium of structured credit investors.

On the equity side, SoFi reached a $500 Mn investment led by Silver Lake for another round of financing, and further extended its distribution channel to community banks by partnering with Promontory Financial. Upstart announced a $33 Mn financing led by Rakuten.

M&A prospects increased in the small business sector this quarter. EJF and Marathon have built sizeable positions in small business lender ONDK. Kabbage is reportedly raising capital to fund acquisitions as well.

The IPO markets are re-opening to non-bank lenders. Elevate Credit (ticker: ELVT) set a $12 to $14 price range and intends to raise up to $107.8 Mn this week.

Strong Credit Facility Deal Flow

Besides securitization, credit facility warehousing is an alternative venue for lenders to source financing; however, the cost of credit facility depends the borrowers’ counterparty risk, asset class, credit performance, and pricing in the competing ABS market.

OnDeck continued to restructure its financing framework. It amended its asset-backed revolving credit facility with Deutsche Bank, extending the maturity date and increasing borrowing capacity. The Class A tranche of the facility has an advanced rate of 85%; and Class B 91%.

Further, Fundation secured an asset-backed credit facility to expand its business from MidCap Financial, a specialty finance firm. Renovate America completed a $100 million credit facility with Credit Suisse to expand its unsecured consumer home-improvement lending product.

Waterfall Asset management provided a £100 Mn credit facility to UK-based Lendable and $100 Mn facility to BorrowersFirst. Victory Park Capital provided a $100 Mn facility to LendUp.

Deal Performance Weakens, Structures Protecting Senior Notes

Elevated charge-offs have contributed to trigger breaches in ten active MPL ABS deals, eight unsecured consumer and one SME deal, representing $1.5 Bn of total issuance volume.

Data integrity continues to be in the mind of all participants in the marketplace lending ecosystem. In December 2016, Structured Finance Industry Group (SFIG) released the ”Green Paper” on data reporting standards. To further bolster SFIG’s effort, PeerIQ offered the industry Data Standards and Best Practices, which summarized the key learnings and best practices we have developed from years of experience as the leading data & analytics provider in the marketplace lending ecosystem.

Definitions and Inclusion Rules

Our Tracker includes all issuances connected to assets originated by marketplace lending platforms, which we define as including both:

(i) Online and other novel technologies to increase operational efficiency, risk accuracy, and borrower experience, and

(ii) Non-deposit funding for lending capital.

We recognize there is rapid innovation in lending channels, and welcome all comments and consideration on inclusion rules.

I. Quarterly Round-up

The first quarter of 2017 witnessed seven securitization deals, totaling $3.0 Bn in new issuance, the largest quarterly total that the MPL ABS market has seen, which brings total issuance to $18.1 Bn. This represents a 20% increase over 4Q2016, and 100% increase over 1Q2016.

Total securitization issuance to date now stands at $18.1 Bn, with 80 deals issued to date (48 Consumer, 22 Student, 1 Mortgage and 9 SME) since September 2013 (Exhibit 1).

Exhibit 1
Cumulative Marketplace Securitizations

Examining issuance by underlying collateral segment, we see large increases in consumer and student relative to the first quarter of 2016, up 76% and 54% respectively. SME remains the smallest segment, but Kabbage’s $525 Mn issuance is the largest SME deal since their previous one in 2014 (Exhibit 2).

Q1 2017:

• SoFi: SOFI 2017-A, SOFI 2017-B, SCLP 2017-1, SCLP 2017-2

• LendingClub: ARCT 2017-1

• Marlette: MFT 2017-1

• Kabbage: KABB 2017-1

This quarter, SoFi priced its largest consumer securitization, which is backed by a $650 Mn pool of consumer loans. SoFi continues to remain active in the student sector as well with two deals this quarter totaling over $1 Bn.

Prior to this quarter, the market had only seen deals backed by LendingClub Non-Prime loans; however, with the Arcadia Receivables Credit Trust 2017-1, we witnessed the pricing of the first deal backed by LendingClub Prime loans. The deal consists of loans with higher average loan balances and higher FICO scores, which resulted in tighter pricing than tranches backed by non-prime loans.

Rating agency participation has continued to be a key driver for strong deal execution. All deals issued in 1Q2017 were rated by at least one rating agency (Exhibit 3 & 4). Again, we expect that the almost all MPL ABS deals will be rated as issuers seek to broaden the base of eligible investors and access cheap funding.

 

Finally, deals continue to increase in average deal size over time, led primarily by SoFi’s large placements. The average securitization deal now stands at $425 Mn for 2017 (Exhibit 5).

 

II. MPL Securitization League Tables

Dealers intensified their participation in MPL deals this quarter with Goldman Sachs, Deutsche Banks, and Guggenheim scoring record-sized deals. Maintaining its top rank in the league table, Goldman Sachs led $625 Mn in total new issuance, an 18% growth from 4Q2016 (Exhibit 6). It executed deals with SoFi and Marlette to maintain its 23% market share in MPL ABS. Further, Guggenheim showed over 200% growth through their role as the sole dealer in Kabbage’s $525 Mn securitization. The top three dealers, Goldman Sachs, Morgan Stanley, and Deutsche Bank have contributed to over 55% of the total market share in MPL space.

As we noted in 4Q2016, deal interest and involvement remains high despite mixed headlines, regulatory uncertainty, and firm-specific considerations throughout 2016. This positive trend continued in 1Q2017 with nine of the top ten lead managers increasing their involvement in MPL issuances. In addition, the industry saw new participant, BAML, lead the two SoFi student deals this quarter.

Risk retention regulations, now in effect, increases the capital burden for issuers and may slow the pace of deal activity. Various risk retention solutions are emerging as we have summarized here.

Turning to the co-manager league table, SoFi remains the clear leader in the co-manager league, increasing their participation value to over $3.4 Bn through their co-manager position on their two student deals (Exhibit 7).

Deutsche Bank and Morgan Stanley’s co-manager participation in these SoFi deals significantly increased their involvement as well. This brings Morgan Stanley even closer to joining the top three participants, which now maintain approximately 65% of the total market share.

From originator league table (Exhibit 8), SoFi is a clear leader and takes over 51% of the market share. The second place Prosper only has 13% of the total pie. Further, SoFi has much large sandbox, including student refi, unsecured consumer and mortgage loans, whereas other originators focus on a single vertical.

We currently observe 143 rated MPL ABS bonds in the market. As of 1Q2017, DBRS leads Moody’s, Kroll, S&P and Fitch in the amount of rated bonds (Exhibit 9). DBRS rated $6.6 Bn Student MPL ABS, or approximately 45% of sub-segment, competing primarily against Moody’s within the student sector. Kroll dominates the Consumer MPL ABS category with 53% market share. The mortgage sub-segment currently has an even split amongst DBRS, Fitch and Kroll.

1Q2017 also saw an increase in ratings actions. For deals issued prior to 2017, there were several ratings updates. Deals from SOFI, EARN, AVNT, and CHAI shelves received ratings upgrades, serving as, yet, another positive sign from the agencies.

CAN 2014-1A was downgraded by both S&P and DBRS as the deal breached the minimum excess spread trigger.

 

III. New Issuance Spreads

New issuance spreads in 1Q2017 continued to tighten, reflecting a healthy risk appetite in the capital markets. We saw a continued preference for senior tranches over riskier subordinated bonds (Exhibit 10). However, in the consumer and student sectors, the term structures flattened from the previous quarter, reflecting stronger investor appetite for riskier tranches.

Kabbage executed a first SME securitization since Q216, backed by a revolving pool of receivables consisting of (i) business loans or (ii) merchant cash advances. The deal features a 36-month revolving period during which time principal collections may be reinvested to purchase additional receivables. The A tranche was priced 50 basis points tighter than deals in Q216; and the B tranche priced 270 basis points tighter. The D tranche priced at 1050 basis points, forming a steep credit curve.

IV. Deal Credit Support Profile

Deal credit performance has weakened, although structural protections continue to protect bondholders. Deteriorating collateral performance has contributed to trigger breaches in ten active MPL ABS deals, eight unsecured consumer and one SME deal, representing $1.5 Bn deal issuance. Despite weakening in credit performance, we have not seen a meaningful uptick in initial credit support for unsecured consumer loan MPL ABS, reflecting growing comfort in the current conservative rating approach by the agencies. The senior and mezzanine tranche initial credit support has been stable for SoFi, LCIT, and other shelves (Exhibit 11). Still, Avant deals have featured increasing credit support at issuance (solid lines are regression lines).

Due to the amortizing nature of the collateral pool, MPL ABS bonds typically exhibit shorter weighted-average life (WAL) and a faster deleveraging profile than other structured products, such as MBS bonds backed by 30-year mortgage collaterals. As deals season and perform as initially expected, the credit supports increases. We quantify the amount of deleveraging by measuring the differences in current and original credit support for the same bond. Exhibit 12 shows that the senior part of capital structure begins to deleverage first then mezzanine tranches (solid lines are regression lines). For instance, Prosper senior bondholders will enjoy a 40% improvement in credit support as the bond seasons.The declining credit support in Loan Depot and CircleBack deals reflectsworse-than expected collateral performance and the structural impact of breaching performance trigger

V. Credit Performance Trends

Consumer credit delinquencies and charge-offs continued to increase during the first quarter, across consumer credit verticals including installment, student, and auto loans. Prime credit card portfolios continue to exhibit low and stable levels of delinquency.

This quarter, we released a Performance Monitor to track the health of marketplace lending loans using bellwether public programs from LendingClub and Prosper. Charge-off rates for both originators continue to be elevated for loans in 2016 vintages. The increase in charge off rates agree with delinquent loan pipelines as loans transition from delinquency to charge-off states. Originators have taken actions including tightening underwriting criteria and raising rates.

Charge-offs remain elevated outside of marketplace lending as well. 60+ delinquency rates were up for private-label credit cards; FFELP and non-FFELP student loans experienced higher delinquency rates as well. In the auto sector delinquency rates on prime and subprime delinquencies may pass pre-crisis levels by end of year.

The primary driver of increased losses for non-bank lenders more generally are

i) a mix-shift to riskier borrowers as measured by credit score,

ii) increased “stacking” behavior (whereby consumers take out multiple loans simultaneously), and

iii) re-leveraging of the consumer balance sheet due to greater availability of credit.

VI. Commentary and Outlook

For 2017, we expect higher volatility from rising rates, increased regulatory uncertainty, uneven policy execution, and an exit from a post-crisis period characterized by unusually benign credit performance.

Although the macro environment remains benign with exceptionally low volatility in capital markets, we expect the next several years to pose significant challenges for risk managers due to the re-normalization of credit performance, intensifying competition for borrowers, and changing payment hierarchy trends.

Re-Normalization of Credit Performance

The Great Recession of 2008 is now more than eight years behind us. We are in the late stage of the current credit cycle.

Consistent with Fair Credit Reporting Act waiting period requirements, derogatory credit items (“derogs”) associated with bankruptcy, foreclosure, and short sales will fall off borrower credit bureau reports in 2017. As a result, we expect an expansion of marketable population to include previously ineligible borrowers.

The re-normalization of credit performance will create significant analytical challenges for underwriting and investment analysis. For instance, models trained on post-crisis borrowers that defended their credit thru the cycle will tend to underestimate losses.

Further, lenders today face new constraints in how they manage their risk from consumer protection regulation such as the CARD Act of 2009, which among other rules, eliminated “universal default” as a mechanism for mitigating risk.

Advanced risk analytics and historical data, such as offerings empowered by PeerIQ analytics and TransUnion dataset, will crucial for risk assessment and management for originations and investors alike.

Changing Consumer Behaviors & Payment Priority

Prior to the Great Recession, mortgages were the first priority of payment on the consumer’s liability. This behavior changed with eir negative equity in home values and the emergence of strategic mortgage default. During the crisis, according to a TransUnion study, consumers holding a mortgage, auto, and bank card consumers tended to default first on their mortgage debt, then credit cards, and finally auto loans – a reversal of prior payment priority trends.

Auto loans emerged at the top of the consumer liability payment hierarchy due to their need in accessing employment and impact of repossession.

The consumer’s relationship with their financial institutions and are changing with the emergence of new technology including ride-sharing services, robo advisors, and online banking services.

Increased competition amongst financial institutions and technology that can render traditional arguments of payment priority obsolete requires heightened monitoring of payment priority trends, and continuous benchmarking of originators performance vs. their peer group.

Not surprisingly, platforms are finding ways to deepening their relationships with customers via community-based service offerings, broader product offerings, and differentiated service.

See for instance SoFi’s dating and job-matching service and new products in insurance and deposits. Lending Club’s announced an expansion into auto-lending. Real estate lender PeerStreet announced a partnership with robo advisor Betterment.

Trigger Breaches Event Stabilizes

In 1Q2017, SoFi SCLP 2015-1, a bespoke bilateral transaction between an investor and provider of financing, was the only additional transaction to breach a deal trigger.

Exhibit 12 summarizes the ten active deals that had breached triggers in MPL ABS sector (nine unsecured consumer and one SME) with $1.3 Bn of total issuance volume. Since the inception of MPL ABS market, we have observed 10% of deals breaching triggers historically.

In the context of ABS transactions, trigger breaches are manifestation of unexpected credit performance, poor credit modeling, or unguarded structuring practice. If an early amortization trigger is violated, excess spreads are diverted from equity investors to senior noteholders with the goal de-risking the senior noteholders as quickly as possible.

From the equity investor’s perspective, tighter triggers allow higher potential equity returns in the absence of any collateral losses. However, equity investors will experience higher return volatility if the collateral pool incurs credit losses. Conversely, less restrictive triggers allow more cushion for losses before the coverage tests are breached, and require greater subordination levels and, hence, more capital.

Trigger breaching events do not necessarily imply credit deterioration of the collateral pool. The art of trigger setting is a core structuring competency for an issuer, warehouse lender, or securitization deal team. A strong analytics toolset offered by firms like PeerIQ will help issuers and investors alike to guard against trigger breach scenarios.

In our 4Q2016 securitization tracker, we anticipated greater participation in the securitization space as one-off issuers seek to become repeat issuers to optimize deal cost and capital market distribution. We expected $11.2 Bn of total new issuance in 2017 for a base case scenario, representing 47% growth in ABS issuance from 2016 (Exhibit 13).

Further, rated securitization has moved from a coming-of-age milestone in the maturation of an originator to a key funding pillar. In 1Q2017, all new transactions were rated by at least one rating agency.

We expected further emergence of repeat issuer. In LendIt 2017, Prosper unveiled its new shelf Prosper Marketplace Issuance Trust (PMIT), expecting first deal in Q2. Further, LendingClub expects to retain $100 Mn of quarterly originations on balance sheet beginning in 2Q17. LendingClub also has its own branded shelf, and we speculate that these loans may potentially be securitized in multi-seller deals. This suggests a $400 Mn in new issuance from a LendingClub shelf.

Under our base case scenario, we expected $130 Mn from LendingClub shelf in 1Q2016 outlook, and we modeled $500 Mn in new issuance under a bullish scenario.

With $3.0 Bn of new issuance in 1Q2017 and recent announcements from new repeat issuers (potentially including Upstart and Prosper) and Avant returning to market, we are on track to realize our year-end projection of $11.2 Bn in new issuance.

Multi-seller trend continues

In 2016, PeerIQ predicted the rise of multi-seller deals as issuers seek to create a reliable path to liquidity for whole loan investors, drive standardization, spread deal costs, and reduce execution risk. The most recent Marlette transaction is notable in that 7 loan sellers vended loans into the deal. We expect a continuation of the multi-seller trend across several platforms.

Bank Partnerships

We argued in prior research that banks can improve their ROE position by funding or financing whole loans from marketplace lenders, and that most banks will choose to partner with marketplace lenders rather than compete.

We believe 2017 will feature a number of bank partnerships with non-banks, and increased competition from traditional banks.

In 1Q2017, Upstart announced their intention to deliver their SAAS technology to assist banks, credit unions, and retailers to originate loans. We expect to see more of this ‘capital light’ business model ahead (see Avant & Regions Bank, or OnDeck and JP Morgan for instance).

We expect traditional banks to cooperate with marketplace lenders to marry their low-cost funding profile with low-cost operations of marketplace lenders.

Higher Volatility from Regulatory Uncertainty

Concerns of heightened regulatory scrutiny last year have given way to a largely constructive outlook, although regulatory uncertainty remains high.

Platforms are sponsoring self-regulatory efforts via trade associations such as SFIG and the Marketplace Lending Association. The message is resonating. In an important milestone for 2016, US Treasury, Federal Reserve, SEC, and the Office of the Comptroller of the Currency (OCC), each publicly acknowledged that marketplace lending is expanding access to credit to traditionally under-served segments.

Markets expect a bias to action from the Trump administration, a more favorable regulatory outlook, and potential for relief on risk retention, bank capital & liquidity requirements, and a reduction in CFPB and SEC enforcement actions.

The President-elect Trump’s transition team, including the nominee for Treasury Secretary, has indicated the new administration wants to “strip back” parts of Dodd-Frank.

Overall, we expect the pace of regulatory relief will be slower than most market participants expect. While the new administration has made some general statements about Dodd-Frank, it’s too early to tell which changes may materialize.

Within MPL specifically, the SEC, the OCC, and state regulators have differing views on jurisdiction and approach to regulation. Moreover, given the cloture rules in the Senate which require 60 votes to overcome a filibuster.

We expect the most likely areas for bank regulatory relief lie within the capital and liquidity framework which has hampered ROE for large banks and community banks alike. Banks have already adapted to incremental consumer protection obligations and shed Volcker-related prop businesses.

Non-bank lenders, especially those adopting a partner funding bank model, will seek regulatory clarity to reduce true lender risk (see for instance Madden v. Midland, Bethune v. LendingClub, or CFPB v. Cash Call) and argue for pre-emption.

In March, OCC Head Thomas Curry, re-affirmed plans to grant special purpose national charters to qualifying FinTech firms. Curry cited “public interest,” a “patchwork of supervision,” and the “great potential to expand financial inclusion” as motivations for the charter.

The charter would offer pre-emption—the ability of chartered FinTechs to export rates across state lines—and avoid the need for disparate state-by-state licensing or originating via partner-funding banks. In constructing the guidelines, the OCC seeks to continue to foster financial inclusion via FinTech innovation, while maintaining public confidence in national banks and the banking system more generally.

The supervisory guidelines in the proposal require, among other provisions, a top-down culture of compliance, a risk assessment and management framework, and to-be-specified capital and liquidity rules. (See here for PeerIQ’s summary of the supervisory guidelines).

Qualifying FinTechs require significant investment in governance, risk management systems, and financial inclusion plans. Chartered FinTechs must adopt a top-down culture of compliance, meet strict supervisory guidelines (summarized here), have seasoned bank professionals on boards and management teams, and seek approval from bank regulators on major business plan changes. More generally, under the charter, FinTechs must culturally resemble the very banks they seek to disrupt. Applications for the charter will be made available to the public on the OCC website.

More fundamentally, the charter does not address the core funding and liquidity issues impacting the sector. The strict qualitative criteria suggest that the charter will be awarded sparingly, case-by-case.

Emergence of 3rd Party Solutions to Promote Confidence

To gain investor confidence, marketplace lenders are now adjusting to the demand from warehouse lenders and whole loan investors for greater transparency and due diligence, including independent reviews, “hot” back-up servicing arrangements, verification, credit validation and heightened data integrity standards.

Further, the recent uptick in delinquency and losses in SME and other sub-segments, in general, leads to a persistent focus on fundamentals, loan modifications, and technology to benchmark performance and combat stacking, TransUnion, for instance, launched a Fraud Prevention Exchange to combat stacking via a tech-enabled consortium of leading non-bank lenders.

Issuers and investors will deepen their investments in 3rd party data and analytics for a variety of investment and distribution activities, such as analyzing deal waterfalls, evaluating deal collateral triggers, monitoring deal performance, coordinating multi-seller securitization deals, and improving investor confidence with loan-level data transparency.

The above trends highlight the need for 3rd party analytics, such as those offered by PeerIQ, to improve transparency, standardization, comparability, with the goal of improving investor confidence and the smooth functioning of ABS markets.

We remain optimistic on the lending ecosystem. The broad secular trends underpinning non-bank lending growth and the global demand for yield remain intact.

About the author: PeerIQ offers portfolio monitoring and loan surveillance, structured finance analytics, third-party reporting, pricing and valuation and advisory services across both whole loans and ABS products.

 

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Report: Marketplace lending 2.0

Report: Marketplace lending 2.0

The story is more than marketplace lending and banks The first report that the Deloitte Center for Financial Services released on marketplace lending in March of 2016 asked essentially one question: Is the convergence between marketplace lenders (MPLs) and banks inevitable?1 And our response was that not only were they already converging, but that there […]

Report: Marketplace lending 2.0

The story is more than marketplace lending and banks

The first report that the Deloitte Center for Financial Services released on marketplace lending in March of 2016 asked essentially one question: Is the convergence between marketplace lenders (MPLs) and banks inevitable?1 And our response was that not only were they already converging, but that there would likely be much more bank-MPL convergence to come in the way of partnerships, joint ventures, mergers and acquisitions (M&A), and lending as a service (LaaS) (in both a branded and white labeling capacity), making disintermediation a moot point.

But 12 months later, we see a picture that is much bigger in scope than just MPL-bank convergence. Marketplace lending is an integral piece of a larger fintech puzzle that is transforming the financial services industry. At its core, what we are calling marketplace lending is simply lending that is enabled through digital platforms that mine data that are increasingly nontraditional, and streamline the loan acquisition, origination and servicing processes.2 We expect that asset classes such as small business, student, and unsecured consumer will move almost completely to digital platforms in the medium term, while other asset classes, such as residential mortgages and auto lending will get there more slowly. The lenders and originators will likely continue to sometimes be separate entities, sometimes the same entity, and will include a variety of types, from banks to online nonbanks to other types of fintechs to traditional technology firms, as well as various types of retail organizations and service providers, in a range of cooperative scenarios including partnering, merging, and LaaS.

Further out, we see a technology platform-enabled lending environment moving from a predictive to a prescriptive analytics stage. In developing their precision for mining, refining and using data effectively to originate loans, lenders should be able to use these data sets to offer tailored financial solutions to customers through a rich ecosystem. Lastly, to comply with any possible future regulation coming from multiple agencies and jurisdictions, MPLs are forming alliances and associations seeming to promote and establish standardization and transparency in order to get ahead of, and work with, regulators.

We cannot ignore the ever-present credit cycle question

A common observation, often repeated, is that marketplace lending, in its current scale, has not yet been tested by a full credit cycle. We did see some headline-grabbing industry events in 2016, but they do not seem to have negatively impacted the industry. Instead, demand remains high with a lot of capital looking for high yield in a still low-interest rate environment. The S&P/ LSTA US Leveraged Loan 100 Index showed a healthy 11.8 percent return from end-January 2016 to end-January 2017,3 so the credit market continues to grow. In short, a sharp downturn in the credit cycle appears unlikely in 2017, unless geopolitical or other events take an unexpected turn. That said, recent spikes in long-term US treasuries yields (the three-year pierced three percent in January),4 while not holding long enough to indicate where the industry is in a credit cycle, is a trend to watch. And with the Federal Reserve System (the Fed) expected to increase interest rates further, it is only natural to assume that we have possibly moved off the height of the credit cycle toward an environment of tighter margins.

The macro credit data volumes and performance are not showing much to indicate this downward trend just yet.

According to the Fed, residential mortgage delinquency rates are relatively high, but they reflect a steady quarterly decline since 2010 when they exceeded 11 percent (Figure 2), and charge-offs have been negligible for some time (Figure 3). Also, unsecured consumer loan delinquencies, although inching up since 2014, are in the two percent range as are charge-offs, both moderate rates for a financial system.5 But while the data at this point are not showing a precipitous fall in the credit cycle in 2017, there are other signs that point to perhaps the beginning of a downward trajectory on the not-too-distant horizon.

The most recent annual Office of the Comptroller of the Currency (OCC) study on lending in the United States is one such sign. The survey reports banks relaxing standards for a fourth consecutive year.6 Predictably, the nature of looser standards has depended on the asset class. For commercial loans, relaxed standards are in pricing, guarantor requirements, and loan covenants, while in retail, lenders are reducing collateral requirements and accepting higher loan-to-value ratios along with higher debt-to-income ratios.

A loosening of underwriting standards has in turn increased the number of subprime borrowers in one asset class in particular: auto lending. Delinquencies have steadily risen in this asset class, with the securitization market driving its growth.7 According to the Fed, motor vehicle loans grew 7 percent year-on-year to over $1.1 trillion in third quarter 2016.8 While not even close to the scale of the financial crisis meltdown of nine years ago fueled by residential mortgage-backed securities (the auto lending market is substantially smaller and less systemic), large banks are involved as managers, trustees, and servicers in securitizations of subprime auto loans, so repercussions of a crash will have some degree of scale.9

With regard to the residential mortgage market, a recent analysis by Deloitte’s US economics team noted that a generational rethinking of ownership in a sharing economy has homeownership on the decline.10 This finding supports a Federal Reserve study that looked for a student debt-homeownership correlation from 1997 to 2010 and found that a “10 percent increase in student loan debt causes a one to two percentage point drop in the homeownership rate for student loan borrowers during the first five years after exiting school.”11 Considering that student debt has increased by 35 percent since 2011 (through November 2016),12 there is reason to believe that homeownership will indeed continue to fall. While this in itself is not a harbinger of a credit-cycle downturn, the combination of that with rising home prices and housing starts, and a recent increase lately in companies entering the market, are signs that the market may be in for a type of bubble as competition increases, supply increases, and demand decreases.

Quantifying marketplace lending

We would be remiss not to put the size of marketplace lending into perspective with the US credit market on a whole. From its beginnings in 2007, MPLs have originated about $40 billion in loans.13 Considering that the average marketplace loan is three to four years old, current outstanding credit is considerably less. Meanwhile, total outstanding unsecured consumer credit alone in the United States is $3.7 trillion.

That said, given its rapid growth, convergence, and expansion into different asset classes, we believe marketplace lending is worth analyzing as another credit cycle indicator. But perhaps the first hurdle to clear in putting a magnifying glass to volume and performance in marketplace lending is that the standard data aggregators upon which credit market statistics trackers depend do not identify marketplace lending as

a separate category. But along with marketplace lending’s growth, companies have emerged in its ecosystem that, due to their business models as platforms for connecting MPLs and investors, have access to marketplace lending data that make a good proxy for the performance and volume of the industry as a whole.

One such company, Orchard, is fully engaged in the complex task of collecting, scrubbing, and synthesizing marketplace lending data that are far from uniformly reported across MPLs. As Figure 4 depicts, originations from a group of MPLs that account for approximately 90 percent (by Orchard’s estimation) of the MPL-originated unsecured consumer lending market have declined for three consecutive quarters, which follow years of constant, and robust, quarter-on-quarter growth.

Given its rapid growth, convergence, and expansion into different asset classes, marketplace lending is worth analyzing as another credit cycle indicator.

Lower originations in the second and third quarters of 2016 are likely a result of a temporary decline in funding after events in early 2016, reflecting a stronger discipline among some MPLs to deliberately manage origination and recalibrate the underwriting process. After this adjustment, we expect to see originations grow in subsequent quarters, at least in the short term.

In summation, marketplace lending will most likely see performance downturn as it experiences its unique market reconciliation in the credit landscape, which we expect to include the convergence we discussed earlier, and some players falling out of the market as the macro credit cycle goes its inevitable natural route on the downward, tighter-margin, less-demand trend of a cycle. But we remain convinced that marketplace lending itself will survive and thrive as a model as the success cases continue to execute with rigor, and become ever more entwined into the lending market as a whole.

As for charge-offs (Figure 6), at least part of the spike could possibly be explained by a lag mismatch. Since it takes a minimum of four months for a loan to charge off, the charge-off numerator is likely in this case to be mismatched to a smaller origination denominator that is not the actual vintages from which the numerator (charge-offs) came.

As noted earlier in the paper, underwriting standards in the US credit market overall appear to be loosening, and at least some MPLs may also be following this natural cycle. Admittedly, at least for now, we do not see this phenomenon happening much among the players from which the data was collected for Figures 4, 5, and 6 as they are mostly the established players that undertook the aforementioned underwriting recalibration.

But we do know that the number of MPLs in the marketplace lending space overall have increased tremendously over just the past 12 months and that underwriting rigor is being sacrificed by some players in this more competitive, crowded market. Anecdotally, we know that non-prime borrowers are being targeted more often; there are now a number of MPLs (again, not reflected in the data collected here) chasing borrowers who had been rejected by other MPLs, especially for personal unsecured installment loans aimed at consolidating credit card debt. So, yes, these market facts and the evidence of data collected showing the market is susceptible to volatility could be interpreted as a credit market that is prone to a softening like any other and could indicate that we are potentially at the cusp, albeit early, of a downward turn.

Changes in the last 12 months that hint at a maturing market

The trend is to focus on a balanced hybrid funding model.

The big names in marketplace lending have raised rates in line with the Federal Funds rate while allowing tighter spreads as their costs of funding rise due to their own credit tightening policies along with an increasingly competitive market. We argue that this behavior is a sign that the industry is maturing. And we posit that the lenders who survive this trend of higher rates and tighter spreads will need some kind of combination of the following attributes: origination scale, reputation, access to balance sheets, access to hybrid investment funding, and technology that can gain efficiencies (for example, doing more with fewer employees and less capital, including, the outsourcing of servicing that is not otherwise important for the expansion of services).

Another sign of maturity is in the MPL funding model. Like large lending institutions, MPLs seem to be moving in the direction of funding that maximizes retention and lowers risk. At its inception, marketplace lending was funded predominantly through selling fractional loans to retail investors. To gain scale, the funding pendulum swung all the way to the other side and MPLs predominantly sold whole loans to private equity, hedge funds and other institutional investors, including banks. Now, the trend is to focus on a balanced hybrid funding model that includes a healthy mix of retail (which can be more “sticky”) and wholesale funding, as well as long- and short-term capital sources, including balance sheet funding and securitization. We believe that MPLs with multiple, tested, vintage portfolios will be at an advantage, especially in a less favorable credit environment, to secure this type of hybrid funding.

On the other side of the funding equation, a more conservative investor with specialization in MPL-originated loans may be becoming the norm—investors who know how and where to kick the tires. In short, the digital financing space may be becoming less of a risk play and more of a viable business model that is being taken seriously by multiple facets of the finance industry. The hedge fund investors who jumped in for the short-term high-yield portfolios including some who leveraged for more yield, have diminished. There are now more investors that have developed expertise in assessing the operations of MPLs and the performance of their loans. At a recent conference, a panel of investors that specialize in online lending platforms agreed that in addition to analyzing operations and controls remotely, it was important to go onsite to meet the staff and look at the actual facilities in which they operate. In particular, the panelists agreed that they were looking at the general operating environment and staff enthusiasm as well as for humility; that staff members know what they don’t know and respect the money they are receiving from investors and giving to customers. They also emphasized assessing an MPL’s technology prowess as much for reporting and cybersecurity capabilities as for acquisition and originations capabilities.16

Not only are many investors physically meeting with lenders and evaluating them with measured, layered diligence, but they are also not shy about outsourcing some crucial due diligence to an ever-expanding marketplace ecosystem, such as market intelligence, legal and tax services. Another sign of maturity in the digital lending space is that more investors are preferring passive (buying portfolios wholesale) versus active (hand picking loans for tailored portfolios) investing in marketplace lending-originated portfolios. We believe this behavior further emphasizes the importance they place on the due diligence regarding the operations of the lenders and not just what is being originated—another trend that may facilitate more institutional investing. Meanwhile, the signs are everywhere that banks have become an integral ecosystem player in marketplace lending. Let’s remember that whether intentional or not, marketplace lending fits into a years-in-the-making effort by many banks to transform their online presence from just a service to a sales channel. As Figure 7 shows, the average MPL-originated loan size has grown, and one of the reasons for this is that banks are often becoming much more involved in putting their balance sheets into the space. Banks—such as Cross River Bank, a vocal proponent for “skin in the game” as a necessity for the future health of marketplace lending—have begun to specialize in partnerships with MPLs. Furthermore, the scenario we explored last year of a bank potentially building an MPL-like lender (online platforms leveraging technology to originate and process loans) has organically become a reality. Examples include Wells Fargo’s FastFlex for small business lending, and Marcus by Goldman Sachs for unsecured consumer lending (Figure 1).

Expanding asset classes also portend the future

In terms of sheer volume, unsecured consumer lending (which increasingly includes student loans) still often rules the marketplace lending landscape by a wide margin, with small business an up-and-coming second, yet other asset classes are also beginning to benefit in meaningful ways.

Small business

On the small business front, the Fed estimates there is still unsatisfied demand to the tune of hundreds of billions of dollars17 and that marketplace lending has begun to make a dent by bringing renewed attention to the market that includes increased bank involvement in the sector either through their own efforts or through MPL relationships.18 Big banks are approving more loans, albeit still only in the 20 percent range of applications according to a monthly survey by Biz2Credit (Figure 8); but the four percent growth rate is quite significant considering they are still the largest lenders to small businesses. The uptick in bank lending is also filling demand for installment loans that enable some small businesses to move away from merchant cash advances (MCAs). Although the MCA market is not going away, the growing trend of bank incumbents becoming more engaged in tech-enabled efforts is proving that there was an unfulfilled demand for lower cost installment products.

Residential mortgage Currently, only four of the top 10 mortgage lenders are banks, credit unions, or other depository institutions, compared to six in 2015 and eight in 2011. In fact, the three banks that accounted for half of all mortgage originations in 2011 now only account for 21 percent. And as one of the two largest nonbanks involved in residential mortgage originations, Quicken Loans also recently introduced an online lending channel, RocketLoans, offering personal installment loans.19

However, the residential mortgage process still often requires reams of antiquated legal documentation and procedures in most states, so MPLs involved directly with residential mortgages have for the most part used their digital platforms to source customers and sell the loans to the large mortgage players. But even in doing this, they have made the market more dynamic and competitive. LendingTree, for example, has heightened competition among banks and MPLs by collecting enough data for assessing a prospects’ credit worthiness to enable more competition among lenders to offer customers pre-approved mortgages that lead eventually to acquisition and origination.20 Most importantly, though, MPLs have begun the process of bringing digitization into an often cumbersome, multi party business.

Commercial real estate

Nonbanks have been investing in the CRE market for a while. Private equity, hedge funds, REITs, and institutional lenders have created and developed a deep nonbank market, so it is only natural that marketplace lending proliferates to make processes even easier for institutional investors. They are doing this, in particular, to invest in projects on central platforms, which has come in the form of “crowdfunding models” that serve to aggregate capital to invest in real estate.

Auto lending

Although auto lending is an asset class that may see a rocky patch in the near future, MPLs are nonetheless focusing on aspects of it that are easily disintermediated. Namely, auto financing is still typically unnecessarily paper-based. Where retail mortgages have multiple parties (for example, bank, attorneys, inspectors, appraisers) that make documentation complex, auto financing does not—often, instead, financed through just the dealer from which the car was purchased. Given the technology-fueled disruptive innovations that have been transforming the automotive industry, such as driverless cars, ridesharing, and connected cars, financing autos through marketplace lending seems a natural step in the innovation evolution, including a dealer building its own online lending platform. These types of lending platforms, for example, could become available with a developed software or hosted service programs that dealers or dealer networks could join.

Securitization is a powerful indicator of marketplace lending’s maturation and future

A burgeoning online-originated, loan-backed securities market may be a telling indicator that marketplace lending is fast becoming a more permanent aspect of the financial services industry. Analysts who pontificated that the end of the Prosper-Citi securitization relationship was a sign that the market for marketplace lending-backed securitization was faltering have been proved wrong.21 The pace and depth of securitizations in the online lending space has not abated. The first securitization happened as recently as October 2013 and the first major credit agency-rated tranches as recently as January 2015.22 Since then, the number of securitizations and cumulative value have grown by double- and triple-digit rates of growth. As of year-end 2016, the total cumulative value was 80 percent higher than 12 months prior (Figure 9).

But more than just growing, securitization has become a viable source for further funding for marketplace lending and a catalyst for its maturing and standardizing. Regarding the latter, the percentage of marketplace-originated loans that were funded through asset-based securities (ABS) was 70 percent in 2016 versus less than 10 percent in 2014. Regarding the former, there are multiple indicators.23

For one, although market interest rates have increased, the loans being securitized often reflect more rigorous underwriting and tighter spreads than in previous years, debunking theories that investors are only interested in online lending as a high-yield play in a low-rate market; in 2016, spreads tightened 50 percent, yet new issuance was oversubscribed.24 That the securitization market already has ecosystem players, such as PeerIQ, that specialize in risk management solutions, portfolio monitoring, and loan surveillance of securitized MPL-originated loans is itself a sign that the market is expanding and maturing. As PeerIQ reports, standardized securitization issues are already becoming a norm as Marlette issued a deal from its mutual fund trust-branded shelf, Lending Club on its LCIT shelf. Upstart and Prosper are initiating their own programs, and SoFi is well along the road of successful repeat issuances, which has reduced its funding costs considerably. Lastly, in terms of market credibility, there are currently 120 marketplace lending-originated ABS.25

None of this is surprising to us, as is evident in the predictions we made in the securitization discussion in our previous paper. We still expect the number of securitizations to exceed 100 in the next couple of years and to continue growing in value by double-digit percentages annually for at least the next two years after that. We continue to expect an increase in the range of investors and sponsors, and there will also likely be more bank partnerships in the loans that are securitized to meet the five-percent risk retention requirements. Lastly, we expect more third-party servicing to raise the level of investor confidence.26

However, while the US Securities and Exchange Commission (SEC) is considering whether a debt instrument can be treated as a tradable security, aforementioned platform ecosystem players are busy building the connector/facilitator platforms and obtaining broker-dealer licenses that are the necessary ingredients for secondary market trading of marketplace lending loans or loan portfolios.

A secondary market in marketplace lending is quite a different story. Although a small one has grown out of the ABS market, the secondary market for online lending otherwise is still very much in its infancy, comprising a few listed funds.

An industry seeking advocacy and standardization

MPLs seem to have astutely grasped that establishing a position in a sector that has historically been rife with economy-breaking transgressions requires proactive measures.

Generally, a sector or industry that self-polices and organizes advocacy mouthpieces is one that is developing for durability.

MPLs seem to have astutely grasped that establishing a position in a sector that has historically been rife with economy-breaking transgressions requires proactive measures.

The marketplace lending industry’s Small Business Borrowers’ Bill of Rights was recognized for accomplishing its intent by the US Treasury in collaboration with the Consumer Financial Protection Bureau, the Federal Deposit Insurance Corporation, the Fed, the OCC, the Small Business Administration, and the SEC. They acknowledged that the Small Business Borrowers’ Bill of Rights was being appropriately proactive to “organize support from online marketplace lenders for transparent pricing and terms, non-abusive products, responsible underwriting, fair treatment from brokers, inclusive credit access, and fair collection practices …”27

The two associations created after publishing our last paper are an extension of this effort: the Marketplace Lending Association (MLA) and the Innovative Lending Platform Association (ILPA). Both seem to be carefully controlling their membership, a sign that they are striving for uniformity in message regarding regulations and promoting standards and protocols, although the MLA added 12 new members recently to capture representation of more asset classes.28 Their challenges are to address governance, funding stability (for example, a healthy mix of institutional, retail, securitization, and long-term), economic downturn readiness (for example, well-run operations will survive, and should be able to pivot seamlessly from origination to servicing), and to promote uniform standards, protocol, and transparency.

We believe that one way online lenders will likely tackle transparency issues will be to continue in the vein of using groundbreaking technology, such as blockchain, to create immutable records to minimize problems such as “stacking” [see Sidebar: Blockchain may go a long way to solving several growing marketplace lending problems].

Blockchain may go a long way to solving several growing marketplace lending problems

Marketplace lending is a natural to join the pioneer use cases for blockchain in that it: 1) is a small and focused enough market with transaction volume for a shared network; 2) comprises tech-savvy companies open to integrating new technology; and 3) comprises nimble players without the size and baggage of complex legacy systems that inhibit integrating blockchain into their core operations.

Blockchain enables near real-time transactions in a “trustless” environment on a distributed ledger that guarantees immutability and irreversibility (if you are unfamiliar with the basic tenets of the blockchain, please refer to Deloitte’s thought leadership white paper

on the topic).29 Hence, in adopting blockchain: MPLs would not have to sacrifice their competitive advantage of fast and efficient digital loan processing as the near-real-time, digital-based feature of recording blockchain transactions would mesh perfectly; the

MPL would gain efficiencies and both the customer and MPL would be assured secure and tamper-proof transactions with the immutability and irreversibility features, and the distributed ledger in a trustless environment would gain efficiencies for the company by eliminating third-party operations.

One particular problem blockchain can address is “stacking,” where borrowers are approved and receive funding from an MPL and immediately afterward or simultaneously apply, are approved and receive funding from another MPL, increasing the probability of default. If a marketplace lending-originated approval to release funds were conducted on a blockchain, transactions would be recorded on a trustless, distributed ledger that prevents duplication and guarantees immutability. Yet, though the transaction in a blockchain is protected and immutable, one might argue that there could possibly be the issue that a borrower could conduct transactions under a different alias. But blockchain can also solve this problem by extending the scope of the distributed ledger to include smart contracts that point to databases with customer data. In the course of an application, a lender would have access to these data on a permissioned basis by a customer, which comes with it the knowledge of whether the customer data has been accessed by another lender.

The biggest hurdle to clear to make distributed ledger technology in lending an effective tool for marketplace lending would be cooperation—that is buy-in from enough players in the industry to participate on a networked, trustless, blockchain. It seems only natural that a technology-driven, innovative industry would be one to embrace a new, ground-breaking technology to speed transactions, reduce operational complexity, and enhance security.

A constantly evolving ecosystem seems critical to this equation of standardization, protocols, and transparency. Banks are ideal compliance providers. Shrewd investors with expertise are likely to want to use platforms and intelligence providers to perform due diligence, and third-party loan servicing is also key; while many MPLs have taken pride in using their technology know-how to service their own originations, organizations with significant scale and expertise are better adapted to service delinquent loans, and the market (that is, investors) is demanding this expertise. A third-party servicing mandate for certain securitization contracts most certainly set the precedent, but third-party servicers are becoming a commonplace factor in marketplace lending of all stripes in line with independent reviews, “hot” backup servicing arrangements, and data validation standards.

Same regulatory issues, but an OCC federal fintech charter would be a game changer

The premise of the decision Madden v. Midland Funding, LLC, that a lender cannot increase the interest rate of a loan that exceeds the usury cap in the borrower’s state of residence applies only in the three states that the Second Circuit Court of Appeals serves: Vermont, New York, and Connecticut.30 Hence, MPLs and banks still have the option to avoid these states or have national bank involvement include balance sheet risk.31 But MPLs are also on alert regarding loans being inhibited for importing even when made by nationally chartered banks in states without usury caps; the Maryland Court of Special Appeals ruled that a payday lender could not market loans online to Maryland residents if those loans had been generated at interest rates above Maryland’s usury caps to Maryland residents by nationally chartered banks. However, the specifics of this case are not a clear-cut worry for MPLs just yet. The payday lender, a nonbank credit servicer, did buy the loans from these banks, and Maryland had a law requiring credit servicers to be licensed in Maryland to service Maryland residents, and the case is also currently on appeal to a higher court.32

That aside, though, bank lending regulations still apply equally to any type of lender, regulations that include the Unfair, Deceptive, or Abusive Acts or Practices, Truth in Lending Act, the Fair Credit Reporting Act, Servicemembers Civil Relief Act, and Anti-Money Laundering and Know Your Customer, to name a few. Also, MPLs must comply with the same collection procedures as any other type of lender.33

The good news is that the assessment by the US Treasury, following its request for information on marketplace lending, acknowledged, with the affirmation of the Federal Reserve, the SEC, and the OCC, that marketplace lending is a welcome and legitimate means to meet both consumer and small-business credit demand that traditional banks had not been serving in the past decade and a half. Other positive developments on the regulatory horizon include the Financial Services Innovation Act, which would create fintech innovation offices in 12 different government agencies and a protection period from regulation while new products are created and tested, and the IRS Data Verification Modernization Act, which would require the IRS to reduce paperwork requirements for borrowers.34

Possibly the most positive development on the regulatory front, the OCC fintech charter, has unfortunately been put into question due to the uncertainty around how it aligns with policymakers on both sides of the aisle in Congress and with a new Republican White House. In theory, the new administration and legislative bodies are deprioritizing federal regulations in favor of state regulations, which would put the OCC charter on shaky ground. The MPL is a prime, and eager, candidate for the OCC’s proposed special-purpose federal charter, which would grant a national bank charter to a fintech that either engages either in fiduciary activities, lending, paying checks, or receiving deposits.35 With a federal charter, MPLs would have the flexibility to import interest rates from the state in which they originate their loan. In fact, without the movement to a national charter, marketplace lending stands the chance of seeing its growth as an industry seriously limited given the complexity that state-by-state regulation adds to an MPL’s operations and geographic scope.

Even without the uncertainty on the policy front, the OCC is waiting on the continuing public comment period on its white paper and then further analysis, following these comments, which will result in a policy statement announcement.36

Thinking optimistically that it does come to pass, we believe that the prime qualifying MPL candidates for this charter would have scale and/or unique and solid technology platforms matched with conservative, reliable, hybrid pools of capital and the ability to operate with fairly tight spreads. Also, compliance with the Community Reinvestment Act (CRA) would favor the more established MPLs as banks have already been engaging with them to meet their CRA requirements, the Lending Club-Citi partnership being a high-profile example.37

Marketplace lending is simply an aspect of the evolution of lending

Marketplace lending has expanded well beyond a question of MPLs converging with traditional banking. Rather, it has taken many shapes to become an integral and expanding part of the lending landscape. Marketplace lending is a prime example of how digitization is becoming integral to financial services’ meaningful market share and operating efficiencies, while meeting rising expectations for customer experience in a digital world.

Yet, as marketplace lending evolves, it is also on the cusp of transformation, again. That there was unmet consumer small loan demand is now not in question,38 but the “low-hanging fruit” that drove MPL growth is well understood and being served by an increasingly diverse group of MPLs and other lenders. Convergence means that banks, MPLs, other organizations, and the marketplace lending ecosystem have likely become more efficient at using technology platforms to originate loans. So now it is up to MPLs who want to retain some degree of autonomy to continue to develop and evolve within the financial services industry with a disciplined approach, including underwriting with rigor, having hybrid funding access (which can include balance sheets), and finding customer scale through tactical partnerships. But let us not forget that there is also ample market opportunity for marketplace lending that does not require scale—providing online lending as a service (LaaS) in either a branded or white labeling capacity. LaaS seems to be a sure ticket to survival, especially with a lending market that includes both small and mid-size traditional banks and credit unions that do not have the operational and/or capital capacity to develop in-house marketplace lending as well as financing arms of parent companies with core operations out of the realm of lending. Imagine the potential, for example, of linking auto-dealer financing to digital lending platforms.

So what about the downturn in the credit cycle that we predict will have an impact on marketplace lending, and won’t this affect the securitization market? Sure, a little and maybe more, for a time. But we believe that marketplace lending is not only here to stay, but will be difficult to identify as its own class as it converges and integrates and becomes a common aspect of lending. So, yes, a credit cycle would change the landscape, but its sturdier aspects, which have already changed lending for good, are expected to remain. Its players and ecosystem could deepen and expand, and securitization and secondary markets that come of it may only grow and strengthen, fueled by the successful market players.

Lastly, there seems to be tremendous opportunity for MPLs in the United States to grow exponentially by going global. Once MPLs work out data integrity kinks and obtain more clarity on regulations to which they may be subjected, it may be only natural that the success stories expand abroad and join markets that are already flowering in Europe and Australia, and growing exponentially in China.

Contacts

Industry leadership

  • Kenny M. Smith, Vice Chairman, US Financial Services Leader, Deloitte Consulting LLP
    +1 415 783 6148, kesmith@deloitte.com

Author

  • Stephen Fromhart, Research Manager, Banking & Securities, Deloitte Center for Financial Services, Deloitte Services LP, Contacts Deloitte Center for Sandeep Gupta Financial Services

General contacts

  • Sandeep Gupta, Partner,Deloitte & Touche LLP, New York
    +1 212 436 5614, sandgupta@deloitte.com
  • Chris Herrmann, Partner, Deloitte & Touche LLP, San Francisco
    +1 415 783 4303, cherrmann@deloitte.com

Deloitte Center for Financial Services

  • Jim Eckenrode, Executive Director, Deloitte Center for Financial Services, Deloitte Services LP
    +1 617 585 4877, jeckenrode@deloitte.com
  • Val Srinivas, Ph.D., Research Leader, Banking & Securities, Deloitte Center for Financial Services, Deloitte Services LP
    +1 212 436 3384, vsrinivas@deloitte.com

Report originally published here.

—————————————

References

1 Stephen Fromhart and Val Srinivas, “Marketplace lenders and banks: An inevitable convergence?” Deloitte Center for Financial Services, March 29, 2016.
2 Marketplace lending and MPL: Not too long ago, a technology platform to connect borrowers and investors was referred to as peer-to-peer lending, but this term was considered too narrow once institutional investors and other investing scenarios entered the equation. The same has now been said of the terms marketplace lending and marketplace lenders (MPLs) as online lenders that fund through balance sheets are technically not tapping a “marketplace” for funding. There is considerable debate on the subject, but in this paper, marketplace lending and marketplace lenders refer also to lenders employing an online (or other digital) lending platform while funding with their own balance sheet.

3 “S&P/LSTA U.S. Leveraged Loan 100

Index,” S&P Dow Jones Indices, accessed

February 14, 2017.

4 US Department of the Treasury, Resource

Center, resource-center/data-chart-center/ interest-rates/Pages/Historic-Yield-Data-
Visualization.aspx

5 “Charge-Off and Delinquency Rates on

Loans and Leases at Commercial Banks,” Board of Governors of the Federal Reserve System, November 18, 2016.

6 “Banks Loosen Standards for the 4th Straight Year – US Regulator,” Reuters
News, December 20, 2016.

7 Aaron Back, “A Warning Flashes on Auto Loans,” The Wall Street Journal, November 8, 2016.

8 “Consumer Credit, October 2016,” Federal

Reserve Statistical Release, December 7,

2016.

9 Joe Cioffi, “A Whole Lot of Hurt in Auto Lending May Be Coming,” American Banker,

December 27, 2016.

10 Patricia Buckley and Akrur Barua, “The US housing market recovery: The past is not prologue,” Deloitte University Press,
November 2016.

11 Alvaro A. Mezza, Daniel R. Ringo, Shane

M. Sherlund, Kamila Sommer, “On the

Effect of Student Loans on Access to

Homeownership Finance and Economics

Discussion Series, Federal Reserve Board,

November 2015.

12 “Federal Reserve Statistical Release G.19: Consumer Credit,” Board of Governors of the Federal Reserve System, February 7, 2017.

13 This $40 billion figure is an estimate based on rounding up the data supplied by Orchard in the last quarter depicted in figure 4 to include asset classes that are not represented in the data

in figure 4.

14 “Federal Reserve Statistical Release G.19: Consumer Credit,” Board of Governors of the Federal Reserve System, February 7, 2017.

15 “Consumer unsecured Q3 2016,” Orchard

Quarterly Industry Report, December 9,

2016. Note: For the purpose of protecting the privacy of Orchard’s clients, we do cannot list the names of the MPLs from which this data was collected.

16 “Key Investor Considerations for Marketplace Loan Investment Strategies,” Marketplace Lending & Investing Conference, September 27-28, 2016.

17 “Small Business Credit Survey 2015,” The Federal Reserve Bank of Atlanta, Boston, Cleveland, New York, Philadelphia, Richmond, and St Louis, March 2016.

18 “Opportunities and Challenges in Online

Marketplace Lending,” US Department of the Treasury, May 10, 2016.

19 Annamaria Andriotis, “Banks No Longer Make the Bulk of U.S. Mortgages,” The Wall Street Journal, November 2, 2016.

20 Peter Rudegeair, “Online Lenders Offer

New Competition for Banks,” The Wall Street Journal, June 28, 2015.

21 Noah Buhayar, “Citigroup Said to Stop

Securitizing Prosper’s Online Loans,”

Bloomberg, April 12, 2016.

22 Tracy Alloway, “P2P Consumer Loans Given Landmark Rating,” Financial Times, January 28, 2015.

23 “Marketplace Lending Securitization Tracker: Q4 2016,” PeerIQ, accessed at www.peeriq.com.

24 Ibid.

25 Ibid.

26 Ibid.

27 “Opportunities and Challenges in Online

Marketplace Lending,” US Department of the Treasury, May 10, 2016.

28 MLA members are Lending Club, Prosper

Marketplace, Funding Circle, Able, Affirm,

Avant, Commonbond, Marlette Funding, PeerStreet, Sharestates, StreetShares, and Upstart. Associate members are dv01, LendIt, and eOriginal. ILPA members are
OnDeck, CAN Capital, and Kabbage.

29 David Schatsky and Craig Muraskin,

“Beyond Bitcoin: Blockchain is Coming to

Disrupt your Industry,” Deloitte University Press, December 7, 2015.

30 Marc Franson and Peter Manbeck,

“The Regulation of Marketplace Lending:

A Summary of the Principal Issues

(2016 Update),” Chapman & Cutler LLP, April 2016.

31 Ibid.

32 Ibid.

33 Ibid.

34 “Marketplace Lending Securitization Tracker: Q4 2016,” PeerIQ, accessed at www.peeriq.com.

35 Lalita Clozel, “OCC Grants New Charter to Fintech Firms with Strings Attached,” American Banker, December 5, 2016.

36 Lalita Clozel, “Does the OCC really have the power to charter fintech firms?”

American Banker, January 18, 2017.

37 Lalita Clozel, “How the OCC Plans to Apply CRA-Like Requirements to Fintech Firms,” American Banker, December 9, 2016.

38 “Opportunities and Challenges in Online

Marketplace Lending,” US Department of the Treasury, May 10, 2016.

 

Digital Lending’s Impact on Home Improvement Financing

Digital Lending’s Impact on Home Improvement Financing

When compared to traditional consumer credit products, home improvement purpose consumer loan performance is declining. We think increased competition from emerging point-of-sale lenders may be contributing to this trend and is indicative of an expansionary phase in the consumer credit cycle. Home improvement borrowers are migrating to digital lending: One survey indicated that roughly the […]

Digital Lending’s Impact on Home Improvement Financing

When compared to traditional consumer credit products, home improvement purpose consumer loan performance is declining. We think increased competition from emerging point-of-sale lenders may be contributing to this trend and is indicative of an expansionary phase in the consumer credit cycle.

Home improvement borrowers are migrating to digital lending: One survey indicated that roughly the same percentage of respondents would consider an unsecured consumer loan and HELOC / HEL to help pay for a home improvement project.

Purpose-focused originators are lending to these borrowers because they are relatively good credit risks: Lending Club (LC) home improvement purpose loans tend to prepay faster, are smaller in size, and attract borrowers with a slightly better credit profile than a comparative cohort, although they tend to charge-off at a slightly higher rate.

LC unsecured home improvement loan performance has diverged from that of traditional secured loans: In 2011-2012, the monthly charge-off rates for consumer loans was roughly equivalent to the deep delinquency rates of auto loans and HELOC’s and the default rate of bankcards. Since then, digitally-originated loan charge-off rates have increased while risk indicators for comparative products have trended down.

The lender specialization trend augurs well for credit markets. Differentiation in asset performance is a hallmark of the expansion phase of the credit cycle and may indicate a further market normalization as unsecured credit warrants a larger risk premium.

Introduction

It is no secret that digital lenders have made major inroads into lending markets in the last decade. In the next decade, we expect their reach to grow further into niche verticals including real estate-related products like home improvement loans. So here, we shine a light on the products consumers use to finance home improvement and pay special attention to digitally-originated consumer loans used for home improvement purposes. In this piece, we (1) recount the evolution of home improvement products, (2) describe the features and claim on assets of traditional and new products used for home improvement, (3) provide a performance comparison of Lending Club home improvement purpose loans to a consumer loan control set, and (4) discuss the LC loan set’s relative risk to more traditional products using Federal Reserve and S&P / Experian data.

When compared to traditional consumer credit products, home improvement purpose consumer loan performance is declining. In the past, these loans had a similar risk profile to HELOC’s and auto loans, but recently their charge-offs have risen on a relative basis. We think increased competition from emerging point-of-sale lenders, who can offer cheaper financing, due in part to a secured claim on property, may be contributing to this trend. Consumers already consider digital lenders and HELOC’s / HEL’s similarly when looking at financing options for home improvement[1]. And home improvement is the third most popular loan purpose for LC loans originated in 2016. With the cost of customer acquisition rising, it only makes sense that digital lenders partner with service providers to offer their products and satisfy consumer needs.

Going forward, the trend towards point of sale lenders may accelerate as further originator specialization occurs and new platforms pick-off desirable customers, like home improvement purpose borrowers. In fact, our analysis indicates that LC home improvement purpose loans tend to prepay faster, are smaller in size, and attract borrowers with a slightly better credit profile than

a comparative cohort, although they tend to charge-off at a slightly higher rate. For purpose-agnostic lenders like Lending Club, the specialization trend may be a net negative, but it augurs well for credit markets. Differentiation in asset performance is a hallmark of the expansion phase of the credit cycle and may indicate a further market normalization as unsecured credit warrants a larger risk premium.

A Look Back

After the 2008 financial crisis, heightened regulatory burdens and cost imbalances contributed to traditional lenders curtailing consumer lending activity. This was especially true for home equity lending products as issuers reeling from write-downs due to home value declines cut issuance. Home equity revolving balances outstanding fell from their peak of $714Bn in Q1 2009 to $472Bn in Q3 2016.

Sensing opportunity, digital lenders filled the consumer credit void and are now projected to generate over $10Bn of ABS issuance in 2017[2]. Digital lenders have proven that issuing consumer, small business, and student credit online is a desired service and a viable business model. Now, we see digital lending pushing into other complex, fragmented, or underserved credit markets, including real estate-related lending. In fact, digital lenders that specialize in underwriting mortgages (e.g. LendInvest), real estate-related lines of credit or financing (e.g. Patch of Land), and point-of-sale appliance (like HVAC units) purchase and installation loans (e.g. Financeit in Canada) have already emerged, and are growing rapidly.

Consumers Have Options

The home improvement financing products consumers use have changed before and after the financial crisis. Pre-crisis, borrowers used HELOC’s, home equity loans, and home improvement loans. Whereas post-crisis, those products were harder to obtain, so some homeowners used digitally-originated consumer loans instead. In fact, one survey indicated that roughly the same percentage of respondents would consider an unsecured consumer loan and HELOC / HEL to help pay for a home improvement project. Below we compare these products by first describing their structure.

 

Home Equity Line of Credit (HELOC’s): HELOC’s allow for a great deal of flexibility in structure. They are typically lines of credit, but can be structured as amortizing loans (with fixed rates, terms, and payments), or loans that require balloon payments at the end of a draw period. They are long-dated, with terms of 5 to 20 years and their size is dependent upon the home value and borrower equity. They are typically variable rate instruments and payments can be designed to be interest-only upfront. HELOC’s are secured by real property claims, even though they are typically non-recourse with respect to a borrower’s personal finances. They are generally subordinate to a mortgage in a bankruptcy and liquidation process. The interest paid on these loans are typically tax deductible for the borrower.

Home Equity loans (HEL’s): Home equity loans are less configurable than HELOC’s although they share many characteristics. Like HELOC’s, these loans are secured by a borrower’s home equity. Therefore, their size depends on the home value and equity amount. They are variable or fixed rate, typically 10-15 years in maturity, and subordinated to the primary mortgage holders claim. The interest paid is typically tax deductible. Unlike HELOC’s, these are typically amortizing installment loans, where borrowers make pre-determined monthly coupon payments.

Consumer installment loans used for home improvement: These loan’s structures are incrementally more rigid than HELOC’s and HEL’s. They have fixed interest rates, terms, and payments. They are generally short-dated (3-5 years in term). Unlike HELOC’s and HEL’s, consumer loans are typically unsecured. These loans are increasingly digitally originated.

Since the crisis, some digital issuers have carved out a niche in this product. For example, One Main Financial, Financeit, and Lightstream issue home improvement loans online, as well as point-of-sale channels.

Home improvement loans: Home improvement loans may have the least flexible structures of the bunch. These loans are issued for the express purpose of financing home improvement projects. The originator may require contractor estimates and home appraisals as inputs to the underwriting process. The originator may also hold back a portion of the loan disbursement until the project is completed or project milestones are reached. The loans typically are less than 7 years in term. Sometimes the loans are secured by liens on property, which are subordinate to the mortgage.

Further Discussion on Claims on Assets

A key difference between HELOC’s / HEL’s / home improvement loans and consumer installment loans used for home improvement is the relative claim on real property. As mentioned in the last section, HELOC’s and HEL’s are secured by the property value, while home improvement loans may be issued with a secondary property lien, which is a weaker claim.

On the other hand, consumer installment loans are effectively unsecured, although there are methods for recovery. Consumer loan originators have the option of filing a judicial lien on the borrower’s property. However, these liens are tenuous and can take years to litigate. Furthermore, they may be beneath other liens emanating from the same project. For example, if a homeowner stops paying his bills, and both a lender and contractor file liens on the property, then the lender’s claim may be subordinated to the contractor’s lien on the home improvement value.

In Canada, some loans used for home improvement purposes are on slightly firmer ground. The Personal Property Securities Act (PPSA) passed in 2012 defines the claims lenders can enforce on debtors.

To paraphrase, lenders can repossess fixtures used in the project, while other building materials embedded within the property can only be recovered through a mechanics lien registration and litigation[3]. For example, a lender can repossess lighting used in a project relatively quickly, whereas a loan used for structural support may require a forced sale to generate recoveries.

Home Improvement Loan Borrowers Are Desirable Customers

Based on our comparison, Lending Club home improvement purpose loans tend to have a higher credit quality and perform marginally better. Before delving into the conclusion further, we describe the data sets used. Both our home improvement and control sets contain loans where the borrowers are homeowners or mortgagers. The loan subset with home improvement as the purpose (HI loan sample) had over 56,000 loans or over $800MM in issuance. Conversely, the control set (non-HI loan sample) did not have a home improvement purpose and contained over half a million loans and over $8Bn in issuance. These two sets have a similar distribution of grades and annual vintages.

On average, LC loans with a home improvement purpose have marginally higher credit quality. Before 2014, LC home improvement borrowers had a median FICO score that was 8 and 15 points higher than the non-home improvement control set and auto-loan borrower scores. However, that difference has fallen for more recently issued loans. Meanwhile, the average LC home improvement loan borrower has a lower DTI over the entire sample set (roughly 3 points). In addition, LC loans used for home improvement are smaller in size (by ~$1,300) and carried lower interest costs for loans originated before 2014. Since then, the average interest cost difference has shrunk substantially, but both are generally cheaper than interest charged for loans on used cars.

Performance-wise, LC loans used for home improvement tend to prepay faster, while maintaining a similar or slightly higher level of defaults. High grade (A thru D) home improvement loans prepay at a quicker pace and charge-off at slightly higher to similar charge-off levels. Meanwhile, lower quality (E thru G) three-year loans tend to prepay slower, but charge-off less than their non-home improvement cohort. Lower grade five-year home improvement loans tend to prepay faster and default at lower levels, but the sample size is small.

When segmenting by vintage, the story is similar. Across vintages the home improvement loans prepay faster, but charge-off slightly more. The only outlier is five-year term, 2013-originated loans, which appear to charge-off at a lower rate after one-year of age.

More Originator Specialization and Fiercer Competition Ahead

LC home improvement loans have become riskier than comparative products with a secured claim on assets. However, this was not the case in 2011-2012, when the monthly charge-off rates for consumer loans was roughly equivalent to the deep (90+ days) delinquency rates of auto loans and HELOC’s and the default rate of bankcards. Since then digitally-originated loan charge-off rates have increased while risk indicators for comparative products have trended down.

Why this divergence in performance? We think an increased competition for desirable prime borrowers may be contributing to the change. Since Lending Club proved the marketplace business model, new platforms have created competition for desirable prime borrowers by reducing interest costs and mitigating losses through securing their claims on property and reducing customer acquisition costs by partnering with contractors at the point of sale. For example, Financeit allows contractors to refer customers to its loan program for minimal cost after project estimates. While some of the decline in the LC home improvement purpose loan borrower’s FICO score may be due to a secular trend lower as consumers levered up their balance sheets, much of it may be due to fiercer competition.


More broadly speaking, unsecured consumer loans underperforming secured loans is a healthy sign for credit markets, but a potential net negative for purpose-agnostic lenders. The fact that defaults were similar previously, may have been an anomaly and indicative of a repair phase in the credit cycle. The fanning out of performance may indicate that credit markets have healed and matured, and that new credit suppliers may be joining the fray. This may be good news for investors, who may anticipate a greater stratification of credit risk going forward, and borrowers, who may enjoy lower costs and more customized products.

Author

Aspire Fintech. Aspire enables Originators and Investors to better access, unlock and empower lending data. Aspire counts Marlette Funding, Progressa and Prosper among its data partners.

Contact Information

Aspire Financial Technologies Inc. 250 University Ave, Suite 231 Toronto, Ontario M3H 3E5
+1 647 317 7180
www.aspirefintech.com

Disclaimer

This material is provided for informational educational purposes solely in connection with a discussion of marketplace lending and a number of potential investment entities, structures and strategies relating to marketplace lending activities, and it should not be construed as investment advice or a recommendation to buy, hold or sell any security or purchase any other investment product or service. Aspire is not registered or licensed, nor are we relying on an exemption from registration in any jurisdiction. This presentation is only being provided to recipients who are “accredited investors” as defined under the Securities Act of 1933 or under applicable Canadian securities legislation.

THIS PRESENTATION AND THE INCLUDED MATERIALS IN NO EVENT CONSTITUTES AN OFFER OR SOLICITATION IN ANY JURISDICTION WHERE OR TO ANY PERSON TO WHOM IT WOULD BE UNAUTHORIZED OR UNLAWFUL TO DO SO. UNDER NO CIRCUMSTANCES SHOULD THIS DOCUMENT BE CONSTRUED AS AN OFFERING MEMORANDUM. NO SECURITIES COMMISSION OR SIMILAR AUTHORITY HAS REVIEWED THIS MATERIAL OR HAS IN ANY WAY PASSED UPON THE MERITS OF ANY SECURITIES REFERENCED IN THIS MATERIAL AND ANY REPRESENTATION TO THE CONTRARY IS AN OFFENCE.

Although certain information contained in this presentation has been obtained from sources believed to be reliable, we do not guarantee its accuracy, completeness or fairness of the information. We have relied upon and assumed without independent verification, the accuracy and completeness of all information available from public sources.

References to any indices, the market environment, benchmarks, historical data, or other measures of relative market performance over a specified period of time are provided for your information only and do not imply that any portfolio or investment will achieve similar results. Forward-looking statements contained in this presentation regarding past or current trends or activities should not be taken as a representation that such trends or activities will continue in the future. We do not undertake any obligation to update or revise any forward-looking statements, whether as a result of new information, future events or otherwise. You should not place undue reliance on forward-looking statements or opinions, which speak only as of the date of this presentation.

We do not provide legal, tax or accounting advice and therefore expresses no view as to the legal, tax or accounting treatment of the information described herein. You should inform yourself as to any applicable legal and tax regulations that may be applicable to you.

No representation regarding the suitability of these instruments and strategies for a particular investor is made.

No part of this presentation may, without our prior written consent, be reproduced, redistributed, copied, published or transferred to any other person. This presentation contains information constituting proprietary commercial and financial information, and any unauthorized disclosure of such information could cause substantial competitive and financial harm to us.

The investment bank behind Prosper’s $5bil raise

FT Partners is Pleased to Announce its Exclusive Role as Strategic and Financial advisor to

in its Loan Purchase Agreement with a Consortium of Institutional Investors affiliates of

for up to

 
 

The investment bank behind Prosper’s $5bil raise

FT Partners is Pleased to Announce its Exclusive Role as Strategic and Financial advisor to

in its Loan Purchase Agreement with a Consortium of Institutional Investors affiliates of

for up to

 

 

Report: Auto loans, credit cards and personal loans reached new milestones in 2016

Report: Auto loans, credit cards and personal loans reached new milestones in 2016

Nearly 12 Million Consumers Gained Access to Credit Products Including Auto, Credit Cards and Personal Loans in 2016 TransUnion report finds personal loans continued to grow in popularity; Baby boomers make up largest share of consumers with a personal loan in 2016 Chicago, Feb. 16, 2017 – Auto loans, credit cards and personal loans reached […]

Report: Auto loans, credit cards and personal loans reached new milestones in 2016

Nearly 12 Million Consumers Gained Access to Credit Products Including Auto, Credit Cards and Personal Loans in 2016

TransUnion report finds personal loans continued to grow in popularity;
Baby boomers make up largest share of consumers with a personal loan in 2016

Chicago, Feb. 16, 2017 – Auto loans, credit cards and personal loans reached new milestones in 2016 as the number of consumers with access to these products continued to grow, according to TransUnion’s (NYSE:TRU) Q4 2016 Industry Insights Report. The report, powered by PramaSM analytics, found that balances rose across all credit products at year-end.

“The consumer credit market performed well at the end of 2016, and total balances rose across every credit product in the fourth quarter following a strong shopping season,” said Nidhi Verma, senior director of research and consulting for TransUnion. “For the past several years, auto lenders have been filling the pent up demand in the subprime risk tier and card issuers have been competing for share of wallet. We observed the results of these strategies manifest in higher credit card and auto delinquency rates at the end of the year. Personal loans continued to grow in originations and balances, and we expect this to remain a popular product for all consumers in 2017.”

Personal loans reached a new milestone at the conclusion of 2016 with total balances topping $100 billion for the first time ever.  While younger consumers have played a major role in the growth of these lending products, TransUnion’s Q4 2016 Industry Insights Report confirmed that, contrary to popular belief, mature borrowers are leading the charge on these loans.

Baby boomers comprised 32.8% of all consumers with a personal loan at year-end 2016 followed by Gen X (31.6%) and millennials (26.6%). In Q4 2013, millennials were just 23.5% of personal loan users, but their share has grown over the past three years to reach 26.6% at the end of 2016.

Consumers with a Personal Loan by Generation

The personal loan delinquency rate was 3.83% in Q4 2016, the highest Q4 reading since Q4 2013 and up from 3.62% in Q4 2015. Originations, viewed one quarter in arrears, declined for the second consecutive quarter. Originations dropped 5.7% from 3.75 million in Q3 2015 to 3.54 million in Q3 2016. “We’ve observed a decline in non-prime lending that we attribute to mid-year FinTech funding challenges and regulatory uncertainty in advance of the election,” added Laky. “We believe that the personal loan market is stabilizing, and have seen balances grow across risk tiers through the end of the year.”

“There is a perception that personal loan growth has been driven by younger consumers, but our data clearly indicate that these loans are appealing to older borrowers,” said Jason Laky, senior vice president and consumer lending business leader for TransUnion. “We believe some of this growth is occurring because interest rates may be lower than other type of loans for certain baby boomer segments.”

Total personal loan balances grew $14 billion between year-end 2015 and year-end 2016, reaching $102 billion. The number of consumers with a personal loan continued to climb steadily and ended 2016 at the highest level since at least Q3 2009. In Q4 2016, 15.82 million consumers had a personal loan.

With fewer millennials comprising the FinTech space than expected, TransUnion explored where they may be shopping for loans.  TransUnion’s data found that credit unions are seeing membership growth with the millennial generation. As of Q4 2016, of all consumers with a FinTech personal loan, just 26.3% were millennials, compared to 30% of credit unions’ personal loan customers. The average credit union personal loan was originated at $5,216, compared to $8,051 for FinTech personal loans. Banks originated the largest personal loans at $11,290.

“The distribution of millennial consumers across lender types represents a healthy, competitive market,” said Laky. “Likely due to their strong relationships with members, credit unions have a slightly larger share of the millennial personal loan borrowers, but our data show that they originate smaller loans than FinTechs and other lenders.”

The personal loan delinquency rate was 3.83% in Q4 2016, the highest Q4 reading since Q4 2013 and up from 3.62% in Q4 2015. Originations, viewed one quarter in arrears, declined for the second consecutive quarter. Originations dropped 5.7% from 3.75 million in Q3 2015 to 3.54 million in Q3 2016. “We’ve observed a decline in non-prime lending that we attribute to mid-year FinTech funding challenges and regulatory uncertainty in advance of the election,” added Laky. “We believe that the personal loan market is stabilizing, and have seen balances grow across risk tiers through the end of the year.”

Mortgage Delinquency Rates Stabilizes in Q4 2016

 Mortgage delinquencies continued to decline in 2016, however signs of plateauing are now evident. The mortgage delinquency rate concluded 2016 at 2.28%, and has now declined every quarter on a quarter-over-quarter basis since Q3 2013. While the delinquency rate dropped 7.3% in the last year, it remained nearly unchanged from Q2 2016 (2.29%) and Q3 2016 (2.30%).

“The mortgage delinquency rate has declined for several quarters, and we are observing a stabilization of delinquency rates. The data suggest, given the current state of credit accessibility, we are reaching a natural floor for delinquencies after years of year-over-year declines,” said Joe Mellman, vice president and mortgage business leader for TransUnion.

Mortgage originations, viewed one quarter in arrears, reached 2.23 million in the third quarter of 2016. A growth rate of 19.0% year-over-year propelled mortgage originations to the highest level since they reached 2.32 million in Q2 2013. The average mortgage balance also reached a post-Recession high of $194,415 in Q4 2016.

“Originations have continued to grow across all risk tiers, which suggests lenders may be warming up to originating mortgages to non-prime borrowers, even though that share of the market remains small at under 4%. In the third quarter, mortgage originations surpassed two million originations for the first time in three years as borrowers continued to take advantage of low interest rates and as economic indicators point to a well-performing economy with low unemployment and wage growth,” said Mellman.  “Throughout 2017, we will observe whether the rise in interest rates will impact mortgage market growth.”

Consumers with an Auto Loan Reach Highest Levels since 2009

The latest Industry Insights Report found that 80 million consumers had an auto lease or loan as of year-end 2016. This marks the highest level TransUnion has observed since at least Q3 2009 as approximately 4.3 million additional consumers took out an auto lease or loan in 2016.

Total auto loan balances continued to grow at the end of 2016, closing the year at $1.11 trillion. The total balance grew 8.3% during 2016, slower than the average growth rate of 11.0% between 2013 and 2015. The average auto balance per consumer also rose slightly to $18,391, up from $18,004 in Q4 2015.

“For the second consecutive quarter, total auto balances had a year-over-year growth rate below 10%, reflecting the slower growth that we are seeing in new car sales,” said Jason Laky, senior vice president and automotive business leader at TransUnion. “In the third quarter, auto originations declined year-over-year for the first time in six years. Prime plus and super prime originations continued to grow in Q3 2016, indicating lenders are beginning to shift their focus away from the riskiest segments, where we’ve seen strong competition among lenders that has put pressure on risk adjusted margins.”

Auto originations declined 0.8% to 7.46 million in Q3 2016, down from 7.52 million in Q3 2015. Subprime originations experienced the largest decline (-3.2%), and prime plus (+1.8%) and super prime (+1.7%) originations grew in the third quarter of 2016.

The auto delinquency rate reached 1.44% to close 2016, a 13.4% increase from 1.27% in Q4 2015. Auto delinquency is at its highest level since the Q4 2009 reading of 1.59%.

Total Credit Card Balances Reach $717 Billion at the End of 2016

TransUnion found that the number of consumers with a credit card balance rose 4.4% in Q4 2016 to 139.2 million, the highest level since 2009. Total credit card balances are now at their highest levels since Q3 2009. The largest growth in credit card balances came from the subprime risk tier where balances rose 10.8% between Q4 2015 and Q4 2016.

In addition, the average card balance per consumer grew 2.8% to $5,486, up from $5,337 at year-end 2015 and the highest level since $5,609 in Q4 2010.

“The credit card business hit several milestones in the fourth quarter as the number of consumers with a card balance rose to the highest level we’ve observedin seven years. Driven by a strong holiday shopping season and a steep rise in consumer sentiment, total credit card balances increased 8% year-over-year and surpassed $700 billion for the first time,” said Paul Siegfried, senior vice president and credit card business leader. “Originations continued to rise across all risk tiers, but subprime originations grew at a more moderate pace.”

Originations, viewed one quarter in arrears, grew 14.1% to 17.52 million in Q3 2016, up from 15.36 million in Q3 2015. Subprime originations grew at 8.6% year-over-year in the third quarter of 2016, compared to an average third quarter growth rate of 26.8% from 2013 to 2015.

The credit card delinquency rate reached 1.79% in Q4 2016, an increase of 12.6% from 1.59% in Q4 2015. This marks the highest Q4 delinquency reading since Q4 2011, when the delinquency rate reached 1.90%. The credit card delinquency rate remains more than a full point below its peak in Q4 2009 (2.97%).

“Card delinquencies experienced a deterioration in the fourth quarter of 2016 as lenders’ subprime portfolios matured and energy-dependent states experienced ongoing challenges,” added Siegfried. “However, we observed a significant slowdown of originations to subprime consumers, which could signal lenders are evaluating their risk management strategies in light of the rising delinquency rate.”

Please visit  George PopescuPosted on Categories Analysis, auto, Baby Boomers, Credit Cards, Featured, Gen X, Millennials, personal loans, Prama, Reports, Transunion

PeerIQ’s Marketplace Lending Securitization Tracker Q4 2016

PeerIQ’s Marketplace Lending Securitization Tracker Q4 2016

Executive Summary Marketplace lending securitization remains a bright spot in the ABS market. Total issuance topped $2.4 Bn this quarter with cumulative issuance now totaling $15.1 Bn. YTD issuance of the sector stands at $7.8 Bn as compared to $4.9 Bn from prior year, a 59% increase. Although MPL origination volumes have declined at some […]

PeerIQ’s Marketplace Lending Securitization Tracker Q4 2016

Executive Summary

Marketplace lending securitization remains a bright spot in the ABS market. Total issuance topped $2.4 Bn this quarter with cumulative issuance now totaling $15.1 Bn. YTD issuance of the sector stands at $7.8 Bn as compared to $4.9 Bn from prior year, a 59% increase.

Although MPL origination volumes have declined at some platforms, the percentage of loans funded through ABS is at a record high of 70%.

The movement towards rated securitizations at larger transaction sizes continues. Further, the growth in average deal size continued, growing to $252 Mn in 2016 as compared to $35 Mn in 2013.

New issuance spreads continued to tighten in—a credit friendly environment for securitization. In 2016, we saw moderate spread compression across senior classes, indicating stable investor appetite for MPL ABS paper in the market.

We estimate $6.3 Bn to $11.2 Bn MPL ABS issuance for 2017. Goldman Sachs, Morgan Stanley, and Citi take top positions on the league tables.

Ratings agencies grow increasingly comfortable with assessing MPL risk. Kroll provided the first rating for a securitization of Madden-Midland loans. DBRS tops the league tables in ratings activity.

We expect higher volatility from rising rates, regulatory uncertainty, and an exit from a period of unusually benign credit conditions. Platforms that can sustain low-cost capital access, build investor confidence via 3rd party tools, and embed strong risk management frameworks will grow and take market share.

Authors:

Wilfred Daye wilfred@peeriq.com

Jianguo Xiao jianguo@peeriq.com

Yishu Song yishu@peeriq.com

Investors should consider PeerIQ as only a single factor in making their investment decision. Please refer to the Disclosure Section, located at the end of this report, for information on disclaimers and disclosures.

Introduction

Marketplace Lending ABS Trends

The fourth quarter of 2016 saw steady growth in MPL securitization. Ten deals priced, totaling $2.4 Bn, another quarter of strong quarterly issuance. Total issuance for 2016 reached $7.8 Bn, as compared to $4.9 Bn for 2015 (59% YOY growth). This growth now brings the total size of MPL securitization issuance volume to date to $15.1 Bn.

Although we continue to see differentiation in credit performance and execution across issuers, overall, investor sentiment has improved dramatically versus a year ago. Spreads on new issue senior consumer MPL ABS have tightened 50% for 2016; and the demand for paper across the capital structure has been over-subscribed for new issuance deals.

The market continued to move towards larger deals with repeat issuers. Further, new issuance pricing spreads tightened, in line with other securitized products, although MPL bonds continue to offer attractive relative value for comparable duration and rated products.

All variations of originators—(i) balance-sheet, (ii) pure marketplace lenders, and (iii) hybrids—have established programs to tap into the ABS markets in 2016. In July, Marlette issued its first unsecured consumer loan MPL ABS deal from its new shelf (MFT). Lending Club introduced the first deal on its branded shelf (LCIT), suggesting a pattern of repeat standardized issuance. Looking ahead in Q1 2017, Upstart and Prosper reportedly intend to inaugurate a securitization program.

Further, SoFi, the largest MPL issuer, has emerged as a model for successful repeat issuance, enjoying a 70 to 110 bps funding cost advantage in senior ABS pricing versus its peers in the student lending category.

Macro Conditions

In a widely expected move, on December 14th, FOMC officials increased the Fed Funds rate by 25 basis points to a target range of between 50 and 75 basis points. The Fed is taking an increasingly hawkish stance due to an improving growth outlook (3.5% annualized growth in Q3), tightening labor market (4.6% unemployment, a nine-year low), and wage growth (91-month high). The ten-year inflation bonds traded at ~2% breakeven rate at the year-end 2016. Further, consumer confidence in the economy is at its highest level since August 2001.

A strong “risk-on” environment has prevailed since the US election. Treasuries have sold off and equity markets haverallied on higher growth and inflation expectations. The UST 10-year bond yield of 2.4% is up a whopping 54 bps since Election Day. Equity markets have rallied and the S&P 500 increased 6.4% post-election.

For 4Q16, overall credit markets have exhibited low volatility and credit conditions remain relatively benign. For instance, implied volatility on 3-month CDX HY options stayed at their historical low of 37%. On-the-run CDX HY traded in a range bound between 375 to 420 basis points. Further, in US CLO market, the median YTD total return (through November 2016) was 2.8% for AAAs, 4.0% for AAs, 5.8% for single-As, and 11.3% for BBBs. The US loan index returned approximately 8.9% from Jan to Nov.

Within the MPL space specifically, the above conditions paired with greater investor acceptance—including the first rated deal consisting of Madden-Midland loans in Lending Club’s LCIT 2016-NP2.

Regulatory Uncertainty Remains High

Concerns of heightened regulatory scrutiny last year have given way to a largely constructive outlook, although regulatory uncertainty remains high.

Platforms are sponsoring self-regulatory efforts via trade associations such as SFIG and the Marketplace Lending Association. The message is resonating. In an important milestone for 2016, US Treasury, Federal Reserve, SEC, and the Office of the Comptroller of the Currency (OCC), each publicly acknowledged that marketplace lending is expanding access to credit to traditionally under-served segments.

Regulatory uncertainty remains high for marketplace lenders that rely on partner-funding bank model. The consensus view is that the risk that courts deem loans originated via the partner-funding bank model as invalid is low, although such an outcome, however remote, is paired with high severity.

In November, Thomas Curry, head of the OCC, announced that the agency would grant national bank charters to qualifying FinTech firms. Curry cited “public interest,” a “patchwork of supervision,” and the “great potential to expand financial inclusion” as motivations for the charter.

The charter would offer pre-emption—the ability of chartered FinTechs to export rates across state lines—and avoid the need for disparate state-by-state licensing or originating via partner-funding banks. In constructing the guidelines, the OCC seeks to continue to foster financial inclusion via FinTech innovation, while maintaining public confidence in national banks and the banking system more generally.

The supervisory guidelines in the proposal require, among other provisions, a top-down culture of compliance, a risk assessment and management framework, and to-be-specified capital and liquidity rules. (See here for PeerIQ’s summary of the supervisory guidelines).

The strict qualitative criteria suggest that the charter will be awarded sparingly, case-by-case, and to FinTechs that do not have going-concern risk. More fundamentally, the charter does not address the core funding and liquidity issues impacting the sector. The OCC is accepting comments on the charter and is expected to release final rules in April 2017.

Finally, there several new legislative proposals relevant to the MPL sector, which we summarize below:

Warehouse financing costs remain elevated due to a limited supply of warehouse finance, limited credit performance data, and concerns on data integrity (see CAN Capital credit facility breach).

We observe credit facility financing costs on unsecured personal loans in the 3.5% to 6.5% range. Financing costs are a function of counterparty risk, data integrity, asset class, credit performance, recourse vs. non-recourse financing, and ability to offload risk via an active ABS market.

 

Access to financing and liquidity continues to be central concern for investors in MPL space. This sentiment was highlighted when equity shares of OnDeck surged 5.4% immediately after the announcement of $200 Mn credit facility with Credit Suisse on December 9th.

Other publicly reported credit facility financings in the quarter include Fundation ($100 Mn line from GS), Solar Mosaic ($250 Mn from DB), and We Lab ($25 Mn line from ING Bank), and Apple Pie (SunTrust Bank).

Trigger Breaches

At the end of Q3, PeerIQ predicted several trigger breaches would take place in the ensuing months.

Trigger breaches are manifestation of unexpected credit performance, poor credit modeling, unguarded structuring practice. The average time to breach performance triggers for consumer MPL ABS deals was approximately 11 month as collateral losses ramped up within the deal.

 

During the past three months, four unsecured consumer and one SME deals had breached triggers. The exhibit below summarizes the ten active deals that had breached triggers in MPL ABS sector (eight unsecured consumer and two SME deals) with $1.3 Bn of total issuance volume (Note: below we excluded two additional retired deals that breached triggers (MPLT 2015-OD2 and MPLT 2015-OD1).

 

 

After two years of seasoning, CAN re-classified a portion of collateral loans that were represented as current but should have been treated as delinquent in CAN 2014-1A transaction—a material breach in representations and warranties. The newly identified delinquent loans also led to breach of the minimum excess spread trigger.

The negative headline associated with trigger breaches increased the cost and availability of funding for originators. The growing number of deals breaching triggers in MPL ABS are leading marketplace lenders taking greater control of their ABS programs to set standards and structure.

Sponsors and ABS investors are using sophisticated 3rd party analytics such as those offered by PeerIQ to independently model deal economics and monitor collateral on an on-going basis, perform 3rd party credit validation and verification.

Data & Standardization

Ratings agencies continued to raise the quality and history of data as key factors in their rating assessments. The paucity of historical data increases the error range on cumulative loss estimates which makes ratings assessment difficult.

Finally, we note that the Structured Finance Industry Group (SFIG) workstream on data reporting standards (including PeerIQ as a key participant) released a “Green Paper” providing for data reporting standards. SFIG also has a workstream on representations & warranties across originators—a welcome development for the sector.

Definitions and Inclusion Rules

Our Tracker includes all issuances connected to assets originated by marketplace lending platforms, which we define as including both:

  • Online and other novel technologies to increase operational efficiency, risk accuracy, and borrower experience, and
  • Non-deposit funding for lending capital.

We recognize there is rapid innovation in lending channels, and welcome all comments and consideration on inclusion rules.

  1. Quarterly Round-up

Despite holiday season slowdown, this past quarter saw ten securitization deals, adding $2.4 Bn in new issuance, consistent with Q3 record deal flow. This represents 23.0% YoY growth in total issuance year-to-date and 3.7% growth from Q3, Indeed, MPL securitization remains a bright spot in the ABS world, with its 23.0% YoY growth.

Total securitization issuance to date now stands at $15.1 Bn, with 72 deals issued to date (43 Consumer, 20 Student, 1 Mortgage and 8 SME) since September 2013 (Exhibit 1).

 

Examining issuance by underlying collateral segment, we see that Consumer and Student have similar volumes, with Consumer continuing to lead with $7.1 Bn issued to date, as compared to Student at $6.0 Bn (Exhibit 2). Issuance activities in the Small-Medium Enterprise (SME) space was muted for the quarter. SME remains the smallest segment with $1.7 Bn total issuance.

There were eight new deals in Q4 2016:

  • SoFi: SOFI 2016-E, SCLP 2016-3, SCLP 2016-5, SOFI 2016-F, SFPMT 2016-1
  • LendingClub: LCIT 2016-NP2, MHMT 2016-LC1
  • Earnest: EARN 2016-D
  • Prosper: INSKT 2016-1
  • CommonBond: CBSLT 2016-B

This quarter, SoFi priced its inaugural residential mortgage-backed securitization, which is backed by a $169 Mn pool of first-lien, fixed-rate, prime residential mortgage loans originated by SoFi. SFPMT 2016-1, led by Barclays, will issue four classes of super senior notes, and two tranches of supportive senior notes. (See PeerIQ newsletter for further analysis.)

Since 2010, the market has issued approximately $40 Bn of prime jumbo securitizations dominated by bank players like J.P. Morgan and Credit Suisse, with combined new issuance market share of over 50%. SoFi’s inaugural prime jumbo securitization had demonstrated its ability to grow through value-added origination across verticals, starting with student loan refinance, unsecured consumer loans, and then mortgages. It also supports our thesis that the demarcation line between FinTech and traditional asset classes will be blurred as this emerging industry grows.

In addition to the expansion into new collateral category prime jumbo mortgages, rating agency blessing continues to be a key driver for strong deal execution. All deals issued in 4Q16 were rated by at least one rating agency (Exhibit 5), except for MHMT 2016-LC1 and INSKT 2016-1. As of today, we track 120 MPL ABS rated by rating agencies, of which, 24% were rated in 4Q16, representing stabilized rating agency participation (Exhibit 6). Again, we expect that the vast majority of MPL ABS to be rated as issuers seek to broaden the base of eligible investors.

 

Of note, in Q4, Jefferies brought the first rated LendingClub Near-Prime securitization LCIT 2016-NP2 into the market. This second LCIT shelf deal is also consisted of Madden-Midland loans in its collateral pool. Given the collateral pool of the LCIT 2016-NP1 (unrated) and NP2 (rated) deals are similar and tranches have the identical credit enhancement, the expected loss of each tranche should also be comparable. However, the pricing for LCIT 2016-NP2 A (BBB-rated) was about 75 basis points tighter than LCIT 2016-NP1 A (Non-rated) tranche.

The emergence of rated securities from LCIT shelf also supports our belief that the rating agencies are more comfortable in rating this nascent industry with more historical performance data. Issuers are more inclined to get their deals rated to expand their investor base and increase their credibility with investors.

On the SME front, despite lack of new issuance for the quarter, OnDeck, the leading SME online lender, closed a $200 Mn DBRS A- rated asset-backed revolving debt facility with Credit Suisse, Prime OnDeck Receivable Trust II. The rating of the Class A Loans reflected the 18% of initial hard credit enhancement comprised of the 1% reserve account and 17% overcollateralization. Additional credit support may be provided from excess spread available in the structure.

Finally, deals continue to increase in average deal size over time, led primarily by SoFi’s large placements. The average securitization deal now stands at $267 Mn for 2016.

  1. MPL Securitization League Tables

We continue to see Goldman Sachs and Morgan Stanley ramping up deal participation aggressively in Q4. Maintaining its one number rank on league table, Goldman Sachs was involved in $3.6 Bn in total new issuance, a 44% growth from 3Q16. GS worked with SoFi, CommonBond, and Earnest and other originators to capture about 23% of the market share in MPL ABS by issuance volume. Further, Morgan Stanley showed 60% growth in deal participation with $3.3 Bn in total volume for the quarter, a 4% increase in market share from 3Q2016. Top three dealers, Goldman Sachs, Morgan Stanley, and Citi, took over about 50% of the total market share in MPL space.

Further, as mentioned in our 3Q2016 tracker, banks had pulled back from financing business in MPL space due to recent headlines, regulatory risks, or firm-specific considerations. Yet, dealers continued to display heightened interest in MPL ABS securitization, intermediating originators’ demand for financing and absolute investors’ desire for yield. In particular, we saw Mizuho Securities participated its first MPL ABS transaction (SOFI 2016-F) with Morgan Stanley in December.

As evidenced by the continued issuance, the impact of Dodd-Frank risk retention rules, effective on December 24, 2016, has not led to an appreciable slowdown in new issuance activities. The risk retention rule had indeed slowed down the issuance activities and led to consolidation for mature securitization markets such as Commercial Mortgage Backed Securities (CMBS) and Collateralized Loan Obligation (CLO) market.

Overall, the rule suggested that securitizers will need to commit approximately $23 Bn of new capital to sustain current overall new issuance securitization market in US.

The negative impact of risk retention is masked and by the growth rate of the MPL ABS market and by dealers’ positioning to gain market share.

Turning to the co-manager league table, SoFi doubled deal volume this quarter to $3.0 Bn. As telegraphed by SoFi, SoFi ended 2016 with twelve new deals across student, personal, and mortgage verticals. SoFi is the co-manager for almost all of its deals, capturing approximately 40% of total new issuance (Exhibit 7).

Deutsche Bank raced head of BoA and took over the second position with $684 Mn of deal volume on the co-manager league table, continuing its growth with strong conviction in supporting the MPL ecosystem. The top three co-managers on the league table took over approximately 60% of the total market share. Greensledge, replacing Credit Suisse, stepped into the six place with $421 Mn in deal participation.

Kroll, S&P and Fitch in the amount of rated bonds. DBRS rated $6.4 Bn Student MPL ABS, or approximately 47% of sub-segment, competing primarily against Moody’s within the student sector. Kroll dominates the Consumer MPL ABS category with about 50% market share. The mortgage sub-segment currently has an even split amongst DBRS, Fitch and Kroll.

III. New Issuance Spreads

New issuance spreads in 4Q16 continued to tighten across capital structures, reflecting a healthy risk appetite in the capital markets. Further, we saw a continued preference for senior tranches over riskier subordinated bonds. As reflected in the exhibit below, this overall senior preference leads to a steepening of the overall term structure, with investors demanding higher premiums for riskier tranches— and this remains true across all loan segments. This is particularly evident in student sub-segment. Credit spread curves shifted downward in parallel, suggesting an overall yield compression and favorable credit environment in 4Q16.

  1. Outlook

The outlook for the year ahead presents new challenges and opportunities for marketplace lenders.

We expect higher volatility from rising rates, increased regulatory uncertainty, and an exit from a post-crisis period characterized by unusually benign credit performance.

We see opportunities for lenders, as consumer borrowers move out of the de-leveraging phase, which expands the total addressable market. We also see increased bank partnerships as banks seek to improve their ROE and cover their cost of capital by partnering with MPL originators.

Higher Interest Rates

High financing costs continue to driving a wedge between would-be whole loan buyers and originators. Higher short-term interest rates from a Fed tightening cycle will further reduce net margins for whole loan investors.

Platforms that can successfully pass on rates to borrowers to offset the reduction in net interest margin reduction, increase operating efficiencies, or improve execution in the ABS market stand to take market share. We expect Fed rate hike as a continuation of a tightening cycle. Higher rates will leader to higher ABS coupons for 2017. Therefore, we expect to see originators re-price interest rates on MPL loans.

Higher Volatility from Regulatory Uncertainty

Although the regulatory outlook is constructive, regulatory uncertainty remains high.

Market participants are optimistic. Financial stocks as reflected by the KBW Index have rallied over 20% since Election Day pricing in greater growth from deregulation and earnings from higher rates. Markets expect a bias to action from the Trump administration, a more favorable regulatory outlook, and potential for relief on risk retention, bank capital

  • liquidity requirements, and a reduction in CFPB and SEC enforcement actions. Markets appear to be pricing in near perfect execution of an idealized regulatory environment. We see this as unlikely.

The President-elect Trump’s transition team, including the nominee for Treasury Secretary, has indicated the new administration wants to “strip back” parts of Dodd-Frank. While the new administration has made some general statements about Dodd-Frank, it’s too early to tell which changes may materialize.

Within MPL specifically, the SEC, the OCC, and state regulators have differing views on jurisdiction and approach to regulation.

Moreover, given the cloture rules in the Senate which require 60 votes to overcome a filibuster, we expect the pace of regulatory relief will be slower than most market participants expect

Re-Normalization of Credit Performance

We are now seven years behind the Great Recession. Consistent with Fair Credit Reporting Act waiting period requirements, derogatory credit items (“derogs”) associated with bankruptcy, foreclosure, and short sales will fall off borrower credit bureau reports in 2017. As a result, we expect an expansion of credit eligible borrowers.

Also, borrowers that defended their credit scores through the cycle have positively self-selected. All things being equal, a borrower with a credit score of 700 today is not as strong as a borrower with a score of 700 after the Great Recession.

For 2017, we expect:

  1. Robust growth—a 47% increase in ABS volumes and greater dealer participation
  1. Continued shift to standardized, repeat issuance
  1. New products and additional modes of distribution
  1. Continued trend of bank partnership
  1. Greater investments in 3rd party solutions to improve investor confidence

Continued Growth in ABS Issuance Volumes

As marketplace lending loan growth rates in the US have accelerated over the last few years, lenders have become increasingly reliant on institutional capital, with many platforms particularly focused on securitization as core pillar of funding.

Rated securitization has moved from a coming-of-age milestone in the maturation of an originator, to an essential pillar for funding new origination. We forecast a 47% growth in ABS issuance under our base case scenario.

The re-normalization of credit performance will create significant analytical challenges for underwriting and investment analysis. For instance, as losses will revert to historical levels, models trained on post-crisis data will tend to underestimate losses.

Further, lenders face new constraints in how they manage their risk from consumer protection regulation such as the CARD Act of 2009, which among other rules, eliminated “universal default” as a mechanism for mitigating risk.

Advanced risk analytics and historical data (such as offerings produced from uniting the TransUnion dataset with PeerIQ analytics) will play an important role in risk assessment and management for originations and investors alike.

Strong ABS Issuance Outlook

As we noted in our prior securitization tracker, numerous factors—platform rate increases, tighter underwriting, spread tightening in the primary and secondary ABS markets—improve the economics for whole loan buyers that fund via securitization.

Exhibit 15 below confirms our thesis that securitization is an essential pillar for the marketplace lending industry. Our analysis finds that the percentage of loans securitized via ABS stands at an all-time high of 70%.

 

Shifting Towards Standardized and Repeat Issuance

We anticipate greater participation in the securitization space as one-off issuers seek to become repeat issuers to optimize deal cost and capital market distribution.

PeerIQ anticipates the rise of contributed collateral “club deals” as platforms seek to dive standardization in deal terms while also offering whole loan investors a quarterly path to liquidity.

The growth in club deals will usher in a higher level of data homogenization, greater consistency in deal structure, increased data integrity, and consistent valuation methods.

New Products and Additional Modes of Distribution

Outside of securitization, additional modes of distribution will emerge. RiverNorth received SEC approval for a MPL-dedicated close-end fund in Q3, signaling the maturation of another major funding source for marketplace lending platforms. Other auxiliary funding channels including asset managers offering term capital, CUSIPs, private equity, and seasoning vehicles will expand the investors base for marketplace lending category.

Bank Partnerships

The seismic regulatory changes post the Great Recession of 2008 has forced the global banking sector to adjust the pre-2008 business model.

We argued in prior research that banks can improve their ROE position by funding or financing whole loans from marketplace lenders, and that most banks will choose to partner with marketplace lenders rather than compete.

We believe 2017 will feature a number of bank partnerships with non-banks, and increased competition from traditional banks.

We expect traditional banks to cooperate with marketplace lenders to marry their low-cost funding profile with low-cost operations of marketplace lenders.

Greater Investments in 3rd Party Solutions

To gain investor confidence, marketplace lenders are now adjusting to the demand from warehouse lenders and whole loan investors for greater transparency and due diligence, including independent reviews, “hot” back-up servicing arrangements, verification, credit validation and heightened data integrity standards.

Further, the recent uptick in delinquency and losses in SME and other sub-segments, in general, leads to a persistent focus on fundamentals, such as credit underwriting and acquisition cost.

Originators and ABS investors will extend their investments in 3rd party data and analytics for a variety of investment and distribution activities, such as structuring deal waterfalls, determining deal collateral triggers, monitoring deal performance, coordinating club securitization deals, and improving investor confidence with loan-level data transparency.

The above trends highlight the need for 3rd party analytics, such as those offered by PeerIQ, to improve transparency, standardization, comparability, with the goal of improving investor confidence and the smooth functioning of ABS markets.

We remain optimistic on the marketplace lending ecosystem. The broad secular trends underpinning non-bank lending growth and the global demand for yield remain intact.

Appendix: Marketplace Lending Securitizations to Date

Ticker Type Originator Shelf Issuer Issue Date Collat Amt Credit Amt Initial WAL Coupo Initial Est. Mood S&P DBR Fitc Kroll Rate
($mm) Support ($mm) (yrs) n Type Coupo Pricing ys S h d
SOFI 2016-F A1 Student SoFi SOFI SoFi 22-Dec-16 131.66 16.1% 40.7 3.22 Floating 1.95 n/a A2 Rated
SOFI 2016-F A2 Student SoFi SOFI SoFi 22-Dec-16 131.66 16.1% 82.8 3.48 Fixed 3.02 n/a A2 Rated
SOFI 2016-F B Student SoFi SOFI SoFi 22-Dec-16 131.66 11.0% 7.2 9.51 Variable 4.45 n/a Baa2 Rated
LCIT 2016-NP2 A Consumer Lending Club LCIT LendingClub 2-Dec-16 121.73 35.5% 85.3 1.6 Fixed 3.00 195 BBB Rated
LCIT 2016-NP2 B Consumer Lending Club LCIT LendingClub 2-Dec-16 121.73 23.0% 16.4 2.4 Fixed 6.00 458 BB+ Rated
SFPMT 2016-1A 1A6 Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 15.0% 84.1 4.8 Variable 3.00 220 AAA AAA AAA Rated
SFPMT 2016-1A 1A8 Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 15.0% 28.0 4.8 Variable 3.00 210 AAA AAA AAA Rated
SFPMT 2016-1A 1AMF Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 6.1% 11.8 4.8 Variable 3.00 250 AAA AAA AAA Rated
SFPMT 2016-1A 2A6 Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 15.0% 23.5 3.66 Variable 2.50 195 AAA AAA AAA Rated
SFPMT 2016-1A 2A8 Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 15.0% 7.8 3.66 Variable 2.50 180 AAA AAA AAA Rated
SFPMT 2016-1A 2AMF Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 6.1% 3.3 3.66 Variable 2.50 215 AAA AAA AAA Rated
SFPMT 2016-1A B1 Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 3.7% 4.0 n/a Variable 3.17 n/a AA AA AA Rated
SFPMT 2016-1A B2 Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 2.3% 2.4 n/a Variable 3.17 n/a A A A Rated
SFPMT 2016-1A B3 Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 1.6% 1.2 n/a Variable 3.17 n/a BBB BBB BBB Rated
SFPMT 2016-1A B4 Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 1.1% 0.9 n/a Variable 3.17 n/a BB BB BB Rated
SFPMT 2016-1A B5 Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 0.6% 0.8 n/a Variable 3.17 n/a B B B Rated
SFPMT 2016-1A B6 Mortgage SoFi SFPMT SoFi 1-Dec-16 168.79 0.0% 1.0 n/a Variable 3.17 n/a 0
SOFI 2016-E A1 Student SoFi SOFI SoFi 22-Nov-16 584.42 16.4% 164.6 2.97 Floating 1.38 85 Aaa AAA Rated
SOFI 2016-E A2A Student SoFi SOFI SoFi 22-Nov-16 584.42 16.5% 203.3 1.25 Fixed 1.63 55
SOFI 2016-E A2B Student SoFi SOFI SoFi 22-Nov-16 584.42 16.5% 155.2 4.63 Fixed 2.49 90 Aaa AAA Rated
SOFI 2016-E B Student SoFi SOFI SoFi 22-Nov-16 584.42 10.6% 37.0 n/a Fixed 3.44 175
SOFI 2016-E C Student SoFi SOFI SoFi 22-Nov-16 584.42 6.7% 24.4 8.43 Variable 4.43 265 Baa2 AL Rated
SCLP 2016-5 A Consumer SoFi SCLP SoFi 18-Nov-16 250.02 25.2% 188.3 1.86 Fixed 3.06 n/a A A+ Rated
SCLP 2016-5 B Consumer SoFi SCLP SoFi 18-Nov-16 250.02 15.1% 25.4 4.98 Fixed 4.55 n/a
INSKT 2016-1 A Consumer Prosper INSKT Insikt 2-Nov-16 24.80 30.7% 17.2 1.02 Fixed 4.00 n/a
INSKT 2016-1 B Consumer Prosper INSKT Insikt 2-Nov-16 24.80 9.2% 5.3 3.23 Fixed 11.00 n/a
EARN 2016-D A1 Student Earnest EARN Earnest 31-Oct-16 174.74 13.5% 51.3 3.69 Floating 2.16 140 A AAL Rated
EARN 2016-D A2 Student Earnest EARN Earnest 31-Oct-16 174.74 13.5% 104.2 3.56 Fixed 2.72 155 A AAL Rated
EARN 2016-D B Student Earnest EARN Earnest 31-Oct-16 174.74 6.1% 13.4 4.16 Fixed 3.80 260 BBB Rated
EARN 2016-D C Student Earnest EARN Earnest 31-Oct-16 174.74 2.9% 5.9 4.33 Fixed 4.39 500 BB Rated
CBSLT 2016-B A1 Student CommonBond CBSLT CommonBond 20-Oct-16 168.63 15.0% 86.7 3.92 Fixed 2.73 155 A1 AAL Rated
CBSLT 2016-B A2 Student CommonBond CBSLT CommonBond 20-Oct-16 168.63 15.0% 64.2 3.79 Floating 2.21 145 A1 AAL Rated
CBSLT 2016-B B Student CommonBond CBSLT CommonBond 20-Oct-16 168.63 5.0% 17.7 4.4 Fixed 4.00 280 BBB Rated
MHMT 2016-LC1 A Consumer Lending Club MHMT Prospect 13-Oct-16 314.14 35.5% 204.2 0.62 Fixed 4.19 336
MHMT 2016-LC1 B Consumer Lending Club MHMT Prospect 13-Oct-16 314.14 23.0% 39.3 1.63 Fixed 6.15 396
MHMT 2016-LC1 C Consumer Lending Club MHMT Prospect 13-Oct-16 314.14 10.0% 39.3 2 Fixed 10.00 n/a
SCLP 2016-3 A Consumer SoFi SCLP SoFi 13-Oct-16 599.94 24.7% 451.7 1.85 Fixed 3.05 200 A A Rated
SCLP 2016-3 B Consumer SoFi SCLP SoFi 13-Oct-16 599.94 14.6% 60.9 4.97 Variable 4.49 233 BBB BBB Rated
SOFI 2016-D A1 Student SoFi SOFI SoFi 19-Sep-16 483.04 29.6% 142.8 3.31 Floating 1.60 95 Aaa AAA Rated
SOFI 2016-D A2A Student SoFi SOFI SoFi 19-Sep-16 483.04 27.8% 134.4 1.22 Fixed 1.53 55 Aaa AAA Rated
SOFI 2016-D A2B Student SoFi SOFI SoFi 19-Sep-16 483.04 26.7% 128.8 5.02 Fixed 2.34 110 Aaa AAA Rated
SOFI 2016-D B Student SoFi SOFI SoFi 19-Sep-16 483.04 6.4% 30.7 8.77 Variable 3.23 175 A1 AAL Rated
SCLP 2016-4 A Consumer SoFi SCLP SoFi 13-Sep-16 223.10 20.5% 178.5 1.96 Fixed 3.18 214 A Rated
SCLP 2016-4 B Consumer SoFi SCLP SoFi 13-Sep-16 223.10 17.0% 7.8 5 Variable 4.83 358 BBB+ Rated
SCLP 2016-4 C Consumer SoFi SCLP SoFi 13-Sep-16 223.10 9.5% 16.7 5.12 Variable 5.92 467 BBB- Rated
CILO 2016-LD1 A Consumer Cross River Bank CILO Ellington 24-Aug-16 112.91 30.0% 87.0 1.18 FIXED 3.96 396
CILO 2016-LD1 B Consumer Cross River Bank CILO Ellington 24-Aug-16 112.91 15.0% 18.7 3.13 FIXED 5.50 550
AVNT 2016-C A Consumer Avant AVNT Avant 16-Aug-16 312.59 56.9% 138.0 0.39 Fixed 2.96 350 A- Rated
AVNT 2016-C B Consumer Avant AVNT Avant 16-Aug-16 312.59 31.5% 79.2 1.56 Fixed 4.92 700 BBB- Rated
AVNT 2016-C C Consumer Avant AVNT Avant 16-Aug-16 312.59 19.3% 38.1 2.5 Fixed 8.83 779 BB Rated
LCIT 2016-NP1 A Consumer Lending Club LCIT LendingClub 4-Aug-16 135.48 n/a 86.7 n/a Fixed 3.75 297
LCIT 2016-NP1 B Consumer Lending Club LCIT LendingClub 4-Aug-16 135.48 n/a 16.7 n/a Fixed 6.50 560
MFT 2016-1A A Consumer Cross River Bank MFT Marlette 2-Aug-16 205.44 72.5% 148.9 n/a Fixed 3.06 225 A Rated
MFT 2016-1A B Consumer Cross River Bank MFT Marlette 2-Aug-16 205.44 8.7% 18.0 n/a Fixed 4.78 385 BBB Rated
MFT 2016-1A C Consumer Cross River Bank MFT Marlette 2-Aug-16 205.44 8.7% 18.0 n/a Fixed 9.09 825 BB Rated
SCLP 2016-2 A Consumer SoFi SCLP SoFi 1-Aug-16 575.52 26.5% 425.9 1.84 Fixed 3.09 215 A A Rated
SCLP 2016-2 B Consumer SoFi SCLP SoFi 1-Aug-16 575.52 17.0% 54.7 4.87 Variable 4.77 365 BBB BBB Rated
EARN 2016-C A1 Student Earnest EARN Earnest 29-Jul-16 200.75 28.3% 56.8 3.62 Floating 2.33 185 AAL Rated
EARN 2016-C A2 Student Earnest EARN Earnest 29-Jul-16 200.75 59.3% 119.0 3.57 Fixed 2.68 180 AAL Rated
EARN 2016-C B Student Earnest EARN Earnest 29-Jul-16 200.75 6.8% 13.7 4.03 Fixed 4.46 340 BBB Rated

 

Ticker Type Originator Shelf Issuer Issue Date Collat Amt Credit Amt ($mm) Initial WAL Coupon Initial Est. Moodys S&P DBRS   Fitch Kroll Rated
($mm) Support (%) (yrs) Type Coupon Pricing
SOFI 2016-C A1 Student SoFi SOFI SoFi 27-Jul-16 467.50 27.5% 128.6 3.26 Floating 1.59 110 Aaa AAA Rated
SOFI 2016-C A2A Student SoFi SOFI SoFi 27-Jul-16 467.50 30.5% 142.5 1.26 Fixed 1.48 65 Aaa AAA Rated
SOFI 2016-C A2B Student SoFi SOFI SoFi 27-Jul-16 467.50 26.0% 121.7 4.98 Fixed 2.36 135 Aaa AAA Rated
SOFI 2016-C B Student SoFi SOFI SoFi 27-Jul-16 467.50 6.4% 29.8 8.49 Variable 3.35 200 A2 AAL Rated
SCLP 2016-1 A Consumer SoFi SCLP SoFi 27-Jun-16 506.40 25.5% 379.8 2.3 Fixed 3.26 238 A A Rated
SOFI 2016-B A1 Student SoFi SOFI SoFi 26-May-16 427.03 23.7% 101.4 3.28 Floating 1.72 120 Aaa AAA Rated
SOFI 2016-B A2A Student SoFi SOFI SoFi 26-May-16 427.03 28.7% 122.7 1.14 Fixed 1.68 80 Aaa AAA Rated
SOFI 2016-B A2B Student SoFi SOFI SoFi 26-May-16 427.03 30.8% 131.5 4.78 Fixed 2.74 145 Aaa AAA Rated
SOFI 2016-B B Student SoFi SOFI SoFi 26-May-16 427.03 5.6% 24.1 8.25 Fixed 3.80 225 A2 AH Rated
ONDK 2016-1A A SME OnDeck ONDK OnDeck 17-May-16 265.96 23.6% 211.5 2.28 Fixed 4.21 325 BBB+ A Rated
ONDK 2016-1A B SME OnDeck ONDK OnDeck 17-May-16 265.96 9.6% 38.5 2.71 Fixed 7.63 670 BB- BBBL Rated
EARN 2016-B A1 Student Earnest EARN Earnest 11-May-16 241.93 27.2% 65.8 3.69 Floating 2.57 205 A A Rated
EARN 2016-B A2 Student Earnest EARN Earnest 11-May-16 241.93 61.9% 149.6 3.5 Fixed 3.02 200 A A Rated
EARN 2016-B B Student Earnest EARN Earnest 11-May-16 241.93 4.0% 9.6 4.17 Variable 4.81 375 BBB BBB+ Rated
AVNT 2016-B A Consumer Avant AVNT Avant 28-Apr-16 344.83 49.1% 179.1 0.56 Fixed 3.92 325 A- Rated
AVNT 2016-B B Consumer Avant AVNT Avant 28-Apr-16 344.83 26.8% 76.7 1.83 Fixed 7.80 700 BBB- Rated
AVNT 2016-B C Consumer Avant AVNT Avant 28-Apr-16 344.83 13.8% 44.8 2.73 Fixed 10.60 1,150 BB Rated
CBSLT 2016-A A1 Student CommonBond CBSLT CommonBond 21-Apr-16 162.72 57.6% 93.8 4.3 Fixed 3.32 225 AH Rated
CBSLT 2016-A A2 Student CommonBond CBSLT CommonBond 21-Apr-16 162.72 29.9% 48.6 4.21 Floating 2.72 225 AH Rated
CBSLT 2016-A B Student CommonBond CBSLT CommonBond 21-Apr-16 162.72 6.6% 10.8 4.2 Fixed 4.00 395 BBB Rated
CHAI 2016-PM1 A Consumer Prosper CHAI Citi 31-Mar-16 314.56 33.0% 212.3 0.97 Fixed 4.65 400 A- A Rated
CHAI 2016-PM1 B Consumer Prosper CHAI Citi 31-Mar-16 314.56 25.1% 24.9 2.44 Fixed 7.67 700 BBB- BBB Rated
CHAI 2016-PM1 C Consumer Prosper CHAI Citi 31-Mar-16 314.56 12.0% 41.2 2.83 Fixed 10.26 1,145 B BB- Rated
CHAI 2016-MF1 A Consumer Marlette CHAI Citi 4-Mar-16 156.50 28.0% 113.5 n/a Fixed 4.48 400 A Rated
CHAI 2016-MF1 B Consumer Marlette CHAI Citi 4-Mar-16 156.50 19.2% 13.7 n/a Fixed 6.64 600 BBB Rated
CHAI 2016-MF1 C Consumer Marlette CHAI Citi 4-Mar-16 156.50 10.5% 13.7 n/a Fixed 10.39 990 BB Rated
SOFI 2016-A A1 Student SoFi SOFI SoFi 4-Mar-16 591.51 22.6% 133.6 3.8 Floating 2.27 200 Aa2 AAA Rated
SOFI 2016-A A2 Student SoFi SOFI SoFi 4-Mar-16 591.51 62.2% 367.9 3.65 Fixed 2.76 205 Aa2 AAA Rated
SOFI 2016-A B Student SoFi SOFI SoFi 4-Mar-16 591.51 8.4% 49.9 4.14 Fixed 3.57 350 Baa2 BBBH Rated
AVNT 2016-A A Consumer Avant AVNT Avant 26-Feb-16 344.91 51.0% 172.4 0.45 Fixed 4.11 350 A- Rated
AVNT 2016-A B Consumer Avant AVNT Avant 26-Feb-16 344.91 30.0% 72.4 1.68 Fixed 7.65 700 BBB- Rated
AVNT 2016-A C Consumer Avant AVNT Avant 26-Feb-16 344.91 14.0% 55.2 2.66 Fixed 9.79 na BB Rated
MPLT 2016-LD1 A Consumer LoanDepot MPLT Jefferies 19-Feb-16 100.00 26.0% 74.0 1.31 Fixed 5.25 451
MPLT 2016-LD1 B Consumer LoanDepot MPLT Jefferies 19-Feb-16 100.00 11.5% 14.5 3.89 Fixed 9.50 849
EARN 2016-A A1 Student Earnest EARN Earnest 10-Feb-16 119.48 29.1% 34.7 3.51 Floating 1.99 215 A Rated
EARN 2016-A A2 Student Earnest EARN Earnest 10-Feb-16 119.48 58.8% 70.2 3.51 Fixed 2.50 215 A Rated
EARN 2016-A B Student Earnest EARN Earnest 10-Feb-16 119.48 5.9% 7.1 3.8 Fixed 2.50 290 BBB Rated
MPLT 2015-OD4 A SME OnDeck MPLT Jefferies 24-Dec-15 151.21 15.0% 134.9 n/a Fixed 3.25 287 A Rated
MPLT 2015-OD4 B SME OnDeck MPLT Jefferies 24-Dec-15 151.21 5.0% 15.9 n/a Fixed 5.25 412 BBB Rated
CHAI 2015-PM3 A Consumer Prosper CHAI Citi 18-Dec-15 299.11 46.5% 161.5 0.78 Fixed 2.56 190 (P)A3 A+ Rated
CHAI 2015-PM3 B Consumer Prosper CHAI Citi 18-Dec-15 299.11 26.5% 59.8 2.2 Fixed 4.31 350 (P)Baa3 BBB+ Rated
CHAI 2015-PM3 C Consumer Prosper CHAI Citi 18-Dec-15 299.11 12.0% 43.4 3.37 Fixed 6.99 525 (P)Ba3 BB- Rated
MPLT 2015-CB2 A Consumer CircleBack MPLT Jefferies 15-Dec-15 151.20 22.0% 119.4 n/a Fixed 5.00 na
MPLT 2015-CB2 B Consumer CircleBack MPLT Jefferies 15-Dec-15 151.20 17.0% 7.6 n/a Fixed 6.50 na
AMPLT 2015-A A Consumer Avant AMPLT Avant 19-Nov-15 194.40 30.0% 136.1 1.07 Fixed 5.00 406
AMPLT 2015-A B Consumer Avant AMPLT Avant 19-Nov-15 194.40 20.0% 19.4 1.66 Fixed 6.75 581
AMPLT 2015-A C Consumer Avant AMPLT Avant 19-Nov-15 194.40 10.0% 19.4 1.66 Fixed 8.75 781
SOFI 2015-D A1 Student SoFi SOFI SoFi 18-Nov-15 573.04 27.0% 154.9 3.86 Floating 2.02 150 Aa2 AAA Rated
SOFI 2015-D A2 Student SoFi SOFI SoFi 18-Nov-15 573.04 58.4% 334.8 3.74 Fixed 2.72 150 Aa2 AAA Rated
SOFI 2015-D B Student SoFi SOFI SoFi 18-Nov-15 573.04 8.1% 46.7 4.64 Fixed 3.59 235 Baa2 BBBH Rated
MPLT 2015-LD1 A Consumer LoanDepot MPLT Jefferies 13-Nov-15 88.28 18.0% 123.0 1.74 Fixed 4.00 381
MPLT 2015-LD1 B Consumer LoanDepot MPLT Jefferies 13-Nov-15 88.28 13.0% 7.5 1.74 Fixed 6.00 506
MPLT 2015-LD1 C Consumer LoanDepot MPLT Jefferies 13-Nov-15 88.28 8.0% 7.5 1.74 Fixed 8.00 706
INSKT 2015-3 A Consumer Prosper INSKT Insikt 4-Nov-15 42.00 n/a 32.0 n/a Fixed 4.50 439
INSKT 2015-3 B Consumer Prosper INSKT Insikt 4-Nov-15 42.00 n/a 9.1 n/a Fixed 9.50 947
CHAI 2015-PM2 A Consumer Prosper CHAI Citi 23-Oct-15 419.76 45.0% 230.9 0.77 Fixed 2.35 195 A3 Rated
CHAI 2015-PM2 B Consumer Prosper CHAI Citi 23-Oct-15 419.76 24.5% 86.1 2.19 Fixed 4.00 275 Baa3 Rated
CHAI 2015-PM2 C Consumer Prosper CHAI Citi 23-Oct-15 419.76 10.5% 58.8 2.97 Fixed 5.96 450 Ba3 Rated

 

Ticker Type Originator Shelf Issuer Issue Date Collat Amt Credit Amt ($mm) Initial WAL Coupon Initial Est. Moodys S&P DBRS   Fitch Kroll Rated
($mm) Support (%) (yrs) Type Coupon Pricing
MPLT 2015-AV2 A Consumer Avant MPLT Jefferies 16-Oct-15 111.01 30.6% 86.3 n/a Fixed 4.00 351
MPLT 2015-AV2 B Consumer Avant MPLT Jefferies 16-Oct-15 111.01 20.7% 12.3 n/a Fixed 5.75 510
MPLT 2015-AV2 C Consumer Avant MPLT Jefferies 16-Oct-15 111.01 10.7% 12.3 n/a Fixed 7.50 685
MPLT 2015-AV1 A Consumer Avant MPLT Jefferies 24-Sep-15 126.52 30.1% 88.5 1.08 Fixed 4.00 316
MPLT 2015-AV1 B Consumer Avant MPLT Jefferies 24-Sep-15 126.52 20.1% 12.6 1.69 Fixed 5.75 495
MPLT 2015-AV1 C Consumer Avant MPLT Jefferies 24-Sep-15 126.52 10.1% 12.6 1.69 Fixed 7.50 670
MPLT 2015-OD3 A SME OnDeck MPLT Jefferies 15-Sep-15 79.63 19.7% 67.7 0.54 Fixed 3.25 281
MPLT 2015-OD3 B SME OnDeck MPLT Jefferies 15-Sep-15 79.63 10.2% 8.0 1.28 Fixed 5.25 na
AVNT 2015-A A Consumer Avant AVNT Avant 12-Aug-15 140.00 26.5% 108.4 1.09 Fixed 4.00 342
AVNT 2015-A B Consumer Avant AVNT Avant 12-Aug-15 140.00 16.0% 15.5 1.69 Fixed 6.00 521
AVNT 2015-A C Consumer Avant AVNT Avant 12-Aug-15 140.00 5.5% 15.5 1.69 Fixed 7.75 721
MPLT 2015-OD2 A SME OnDeck MPLT Jefferies 12-Aug-15 73.06 15.5% 59.0 0.42 Fixed 3.25 289
MPLT 2015-OD2 B SME OnDeck MPLT Jefferies 12-Aug-15 73.06 5.5% 6.9 0.97 Fixed 5.25 na
CHAI 2015-PM1 A Consumer Prosper CHAI Citi 5-Aug-15 420.90 46.0% 227.3 0.71 Fixed 1.85 140 A3 Rated
CHAI 2015-PM1 B Consumer Prosper CHAI Citi 5-Aug-15 420.90 25.5% 86.3 2.08 Fixed 2.93 200 Baa3 Rated
CHAI 2015-PM1 C Consumer Prosper CHAI Citi 5-Aug-15 420.90 10.5% 63.1 3.25 Fixed 5.01 385 Ba3 Rated
SOFI 2015-C A1 Student SoFi SOFI SoFi 4-Aug-15 447.56 30.5% 136.5 3.81 Floating 1.57 105 Aa2 AAA Rated
SOFI 2015-C A2 Student SoFi SOFI SoFi 4-Aug-15 447.56 56.0% 250.8 3.67 Fixed 2.51 98 Aa2 AAA Rated
SOFI 2015-C B Student SoFi SOFI SoFi 4-Aug-15 447.56 6.8% 30.3 5.41 Fixed 3.58 184 Baa2 BBBH Rated
INSKT 2015-2 A Consumer Prosper INSKT Insikt 10-Jul-15 4.50 n/a 3.6 n/a Fixed 4.50 438
INSKT 2015-2 B Consumer Prosper INSKT Insikt 10-Jul-15 4.50 n/a 0.8 n/a Fixed 9.50 946
CBSLT 2015-A A1 Student CommonBond CBSLT CommonBond 24-Jun-15 105.00 91.8% 96.4 n/a Fixed 3.20 165 Baa2 AH Rated
SOFI 2015-B A1 Student SoFi SOFI SoFi 9-Jun-15 441.18 33.2% 146.7 3.75 Floating 1.57 105 Aa3 A AAH Rated
SOFI 2015-B A2 Student SoFi SOFI SoFi 9-Jun-15 441.18 53.4% 235.4 3.59 Fixed 2.51 105 Aa2 A AAH Rated
SOFI 2015-B B Student SoFi SOFI SoFi 9-Jun-15 441.18 6.8% 29.8 5.14 Fixed 3.52 165 Baa3 BBB Rated
MPLT 2015-OD1 A SME OnDeck MPLT Jefferies 4-Jun-15 52.08 15.0% 44.3 0.58 Fixed 3.25 266
MPLT 2015-OD1 B SME OnDeck MPLT Jefferies 4-Jun-15 52.08 5.0% 5.2 1.19 Fixed 5.25 na
MPLT 2015-CB1 A Consumer CircleBack MPLT Jefferies 3-Jun-15 110.06 22.0% 99.9 n/a Fixed 4.00 312
MPLT 2015-CB1 B Consumer CircleBack MPLT Jefferies 3-Jun-15 110.06 17.0% 6.3 n/a Fixed 6.00 511
ECLT 2014-1 A Consumer Lending Club ECLT Eaglewood 1-May-15 150.00 n/a 120.0 2.25 Fixed 3.50 260
ECLT 2014-1 B Consumer Lending Club ECLT Eaglewood 1-May-15 150.00 n/a 22.5 2.54 Fixed 5.33 429
INSKT 2015-1 A Consumer Prosper INSKT Insikt 31-Mar-15 4.31 n/a 3.7 n/a Fixed 4.00 396
BLT 2015-1 A Consumer Prosper BLT Blue Elephant 25-Mar-15 60.90 n/a 55.0 0.96 Fixed 3.12 275
BLT 2015-1 B Consumer Prosper BLT Blue Elephant 25-Mar-15 60.90 n/a 8.9 2.56 Fixed 5.56 475
BLT 2015-1 C Consumer Prosper BLT Blue Elephant 25-Mar-15 60.90 n/a 3.6 n/a Fixed 0.00 n/a
GLCT 2015-A A Consumer Prosper GLCT Garrison 2-Mar-15 190.26 n/a 154.1 1.52 Fixed 3.96 324
GLCT 2015-A B Consumer Prosper GLCT Garrison 2-Mar-15 190.26 n/a 9.4 1.52 Fixed 5.43 472
GLCT 2015-B A Consumer Prosper GLCT Garrison 2-Mar-15 120.58 n/a 97.4 1.52 Fixed 3.96 324
GLCT 2015-B B Consumer Prosper GLCT Garrison 2-Mar-15 120.58 n/a 5.9 1.52 Fixed 5.43 472
CCOLT 2015-1 A Consumer Prosper CCOLT BlackRock 9-Feb-15 306.71 23.5% 281.3 1.05 Fixed 2.82 240 Baa3 Rated
CCOLT 2015-1 B Consumer Prosper CCOLT BlackRock 9-Feb-15 306.71 11.0% 45.4 2.86 Fixed 5.21 395 Ba3 Rated
SOFI 2015-A A1 Student SoFi SOFI SoFi 29-Jan-15 313.80 n/a 151.5 3.89 Floating 1.72 125 A2 A AA Rated
SOFI 2015-A A2 Student SoFi SOFI SoFi 29-Jan-15 313.80 n/a 162.3 3.47 Fixed 2.42 125 A2 A AA Rated
GLCII 2014-A A Consumer Lending Club GLCII Garrison 29-Dec-14 153.00 n/a 109.8 1.35 Fixed 4.00 355
GLCII 2014-A B Consumer Lending Club GLCII Garrison 29-Dec-14 153.00 n/a 9.5 1.35 Fixed 6.00 555
INSKT 2014-2 A Consumer Prosper INSKT Insikt 22-Dec-14 7.50 n/a 7.1 n/a Fixed 4.00 396
INSKT 2014-2 B Consumer Prosper INSKT Insikt 22-Dec-14 7.50 n/a 0.6 n/a Fixed 9.00 895
SOFI 2014-B A1 Student SoFi SOFI SoFi 10-Nov-14 303.20 n/a 105.7 3.89 Floating 1.77 125 A2 A AAL Rated
SOFI 2014-B A2 Student SoFi SOFI SoFi 10-Nov-14 303.20 n/a 197.5 3.3 Fixed 2.55 130 A2 A AAL Rated
CANF 2014-1A A SME CAN Capital CANF CAN Capital 17-Oct-14 200.02 n/a 171.0 2.9 Fixed 3.12 210 A A Rated
CANF 2014-1A B SME CAN Capital CANF CAN Capital 17-Oct-14 200.02 n/a 20.0 3.4 Fixed 4.26 221 BBB- BBBL Rated
KABB 2014-1RT A22 SME Kabbage KABB Kabbage 25-Sep-14 n/a n/a 575.3 2.56 Floating 3.27 209 A- Rated
KABB 2014-1RT B2A SME Kabbage KABB Kabbage 25-Sep-14 n/a n/a 168.6 2.56 Floating 10.52 907 BB- Rated
KABB 2014-1RT B2B SME Kabbage KABB Kabbage 25-Sep-14 n/a n/a 0.0 2.56 Fixed 3.00 192 BB- Rated
KABB 2014-1RT B2C SME Kabbage KABB Kabbage 25-Sep-14 n/a n/a 21.1 2.56 Floating 13.52 1,234 B+ Rated
GARST 2014-A A Consumer Prosper GARST Garrison 18-Jul-14 45.54 n/a 36.9 1.52 Fixed 3.00 233
GARST 2014-A B Consumer Prosper GARST Garrison 18-Jul-14 45.54 n/a 2.3 1.52 Fixed 4.00 333
SOFI 2014-A A1 Student SoFi SOFI SoFi 14-Jul-14 280.69 n/a 125.5 3.69 Floating 2.12 160 A A Rated
SOFI 2014-A A2 Student SoFi SOFI SoFi 14-Jul-14 280.69 n/a 125.5 3.72 Fixed 3.02 165 A A Rated

 

Ticker Type Originator Shelf Issuer Issue Date Collat Amt Credit Amt ($mm) Initial WAL Coupon Initial Est. Moodys   S&P DBRS   Fitch    Kroll Rated
($mm) Support (%) (yrs) Type Coupon Pricing
GLCT 2014-A A Consumer Prosper GLCT Garrison 2-Jul-14 169.21 n/a 147.6 1.53 Fixed 3.00 253
GLCT 2014-A B Consumer Prosper GLCT Garrison 2-Jul-14 169.21 n/a 9.0 1.53 Fixed 4.00 353
INSKT 2014-1 A Consumer Prosper INSKT Insikt 28-May-14 n/a n/a 7.1 n/a Fixed 3.50 345
ONDK 2014-1A A SME OnDeck ONDK OnDeck 8-May-14 183.20 n/a 156.7 2.32 Fixed 3.15 250 BBB Rated
ONDK 2014-1A B SME OnDeck ONDK OnDeck 8-May-14 183.20 n/a 18.3 2.8 Fixed 5.68 477 BB Rated
SOFI 2013-A A Student SoFi SOFI SoFi 23-Dec-13 151.80 n/a 151.8 4.35 Fixed 3.75 245 A Rated
INSKT 2013-2 A Consumer Prosper INSKT Insikt 17-Dec-13 n/a n/a 2.6 n/a Fixed 4.25 421
INSKT 2013-2 B Consumer Prosper INSKT Insikt 17-Dec-13 n/a n/a 0.6 n/a Fixed 11.00 1,096
INSKT 2013-1 A Consumer Prosper INSKT Insikt 4-Oct-13 1.57 n/a 1.1 n/a Fixed 4.50 444
INSKT 2013-1 B Consumer Prosper INSKT Insikt 4-Oct-13 1.57 n/a 0.3 n/a Fixed 12.00 1,197
ECLT 2013-1 A Consumer Lending Club ECLT Eaglewood 26-Sep-13 100.00 n/a 75.0 2.37 Fixed 4.30 371
ECLT 2013-1 B Consumer Lending Club ECLT Eaglewood 26-Sep-13 100.00 n/a 24.0 2.44 Fixed 8.00 739

About the author: PeerIQ offers portfolio monitoring and loan surveillance, structured finance analytics, third-party reporting, pricing and valuation and advisory services across both whole loans and ABS products.

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