How Machine Learning Will Transform P2P Lending

artificial intelligence machine learning p2p lending

Artificial Intelligence, fondly known as AI, is the approach and research field of creating machines that are able to replicate human intelligence. Although we aren’t yet teeming with robots as intelligent as humans, we have managed to build: Intelligent programs that can beat reigning Go world champs Virtual assistants like Siri and Google Assistant to […]

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artificial intelligence machine learning p2p lending

Artificial Intelligence, fondly known as AI, is the approach and research field of creating machines that are able to replicate human intelligence.

Although we aren’t yet teeming with robots as intelligent as humans, we have managed to build:

With the passing of time, more and more fields are benefiting from AI and machine learning (ML). The latter is a specific form of AI that deals with devising strategies and methods for allowing a machine to learn from the available and historical data, all to find better solutions for existing problems.

Among the various markets growing by leaps and bounds with machine learning is marketplace lending. AI and machine learning have enabled this sector to automate a variety of processes, shrink operating costs, and make life easier for both lenders and borrowers.

In the past two decades, there has been a tremendous surge in people interested in learning ML as a hobby as well as a professional career choice. Before diving deeper into the subject of how machine learning will transform P2P lending in the future, let’s first get ourselves a brief understanding of ML.

The Craft of Machine Learning a.k.a. ML

Machine learning, or ML, is a subfield of artificial intelligence that follows the notion that machines can learn and adapt better from experience rather than from extensive programming.

Although machine learning is a subset of AI, there are several notable differences between AI and ML. Hence, both fields are considered to be distinct. This analogy is followed by deep learning and machine learning too, where the former is the subfield of the latter.

Today, we are surrounded by many instances of ML, varying from Apple’s Face ID and facial recognition in general to navigational and shopping recommendations generated by virtual assistants. ML also had a profound impact on the financial industry, especially the lending sector, which we are going to discuss in the upcoming sections.

The Present Impact of Machine Learning on P2P Lending

The ongoing evolution of technology and the emergence of tech innovation is having a greater impact on the financial services industry. From designing impactful business models and enhancing the customer experience to cutting costs, the benefits are many.

Fintech firms and the Internet are providing platforms to even niche products to get interested financial supporters as well as potential customers to grab their side. Peer-to-peer financing isn’t only about crowdfunding, but also includes P2P lending.

A bank offers P2P lending by allocating capital from the lending party to the borrowing party while acting as the broker, as well as the risk mitigator. The higher the risk, the higher is the imposed interest rate. Fintech organizations have made this simpler and more effective.

Several Fintech P2P lending firms like LendingClub and Prosper offer a platform where individuals gain interest by lending money to the ones who require it. These firms make money by taking a small fee for making the connection among the two parties possible.

As an alternative to submitting a traditional application, borrowers present compelling stories about why they need the capital via fintech platforms.

Other than the aforementioned, here are some of the most important benefits presently enjoyed by the P2P lending industry thanks to adding the art of machine learning to its arsenal:

1. Easy Identification of Defaulters

As the financial sector grows, so does the number of defaulters and bad loans. This has led the financial institutions to be more careful than ever to spot and avoid defaulters.

Machine learning, with its predictive analysis, can help lenders identify early the borrowers who are highly likely to default later. An early deduction of defaulters can significantly lessen the credit risks, which, in turn, reduces the total number of actual defaulter cases.

2. Hastening the Loaning Process

Of course, lending is one of the most complex business processes. This is the prominent reason why it is a long and tiresome process to accomplish. Thanks to its intuitive analysis, machine learning can remove the redundant parts and, hence, speed up the whole process.

An automated workflow offers lenders a competitive edge against the competition as there is less space for human error. Thus, lenders can process mammoth workloads in shorter time periods. This helps them not only to maximize profit, but also to better serve their clientele.

3. Lessening/Eradicating Errors

Lending involves a lot of documentation. From the very early stage of loan origination to underwriting, most of the steps involved demand preparing and verifying several documents. Doing it manually not only increases the overall time required but also makes it prone to errors.

Machine learning comes in handy here by adding automation to the process, prompting for human intervention where required the most, and thus, streamlining the complete loan cycle.

4. Reducing Operating Costs by Automating Several Aspects of the Lending Process

There are several aspects contributing to the operating costs of a financial institution. Among them, the most notable aspect is loan decisioning. In most cases, the loan arrives at a positive lending decision irrespective of the loan amount.

Machine learning is able to automate the process, consolidate the entire data, and process the same by taking into account a number of factors. Moreover, ML performs a credit check and enhances the overall experience and quality of the process while reducing the overall costs.

5. Simplifying the Whole Process

The traditional way of lending is a very complex process. Hence, it is obvious for lenders to look for options that can simplify the conventional lending workflow (i.e. decreasing the total time required, lowering the overall cost involved, and smoothing out the whole process).

Machine learning helps in simplifying the whole lending process by:

  • Automating various aspects of the business process with customizable rules engine
  • Easing the borrowing process
  • Ensuring straightforward processing of loan applications
  • Standardizing the entire process

The Upcoming Impact of Machine Learning on P2P Lending

With time, artificial intelligence and machine learning are getting bigger and better. This is evident by the fact that there’s a continuous rise in candidates interested in learning AI in general, and ML in particular.

Although machine learning has already started revolutionizing the way P2P lending works, it will continue getting better and more beneficial for the craft. Following are some projections about how ML will impact lending in the near future:

  • Ability to self-modify with little to no requirement for human intervention
  • Better means of filtering irrelevant data from relevant data
  • Completely automating the underwriting process
  • Even faster application, approval, and funding processes
  • Helping lenders to see the bigger picture by insights drawn using efficient ML algorithms
  • Increasing the outreach of lenders and identifying potential lenders
  • Introducing younger people to the lending-borrowing culture
  • More stringent safety protocols to safeguard sensitive information
  • Offering insightful decisions with a higher probability of success
  • Providing capital to more and more honest borrowers
  • Recognize patterns in a specific dataset to help build effective business models
  • Using more number of data points to better check the likelihood of the borrower repayment and estimating the time period of the lender getting the amount back

Challenges Posed by Machine Learning Algorithms

Although machine learning provides access to a variety of avenues for financial institutions, it still needs to do the heavy lifting in some areas to further improve. Three of the most important aspects of ML requiring effort are discussed as follows:

1. Concealed and Undesired Bias

Bias is an inseparable aspect of machine learning algorithms. No ML algorithm can exist bias-free. Although a small degree of bias can be easily managed, the bigger it becomes, the more difficult it gets to fix the same.

Chances for machine learning algorithms to develop concealed and undesired biases from the same data used to train them do exist. Hence, human intervention becomes absolutely necessary to ensure that apposite suggestions or predictions are made by the ML systems.

2. Undetected Errors

Machine learning aims to minimize human interference as much as possible. Hence, it kind of makes humans dependent on machines. Most times, they are free from errors. However, there is room for some errors to slip through.

In case of errors that the machines aren’t able to correct themselves, human intervention becomes mandatory. However, manually identifying and correcting complex algorithms can be arduous and time-consuming.

As lending is an important aspect of the economy, even the smallest of errors being unintentionally passed through can propagate through the system and lead to a huge, undesirable consequence.

3. Time Requirement for Developing Precise Predictions

Machine learning algorithms rely greatly on historical data. The accuracy of the predictions developed by ML algorithms is directly proportional to the total time they spend interacting with the data. Therefore, making precise predictions instantly isn’t possible.

It is mandatory to feed historical data as well as the new data to the ML system continuously for ensuring that the predictions produced by the same are accurate and reliable.

Afternote

There is no denying the fact that the advent of artificial intelligence and machine learning has completely altered the way the lending sector works today. It has become faster, bigger, and better than ever with continuous improvement.

Borrowers and lenders, irrespective of the capital involved, aim to have as few formalities as possible. Though it seems unachievable with the traditional form of lending, the technology-backed lending strives to move as much closer to it as possible.

Fintech firms leverage the latest technological innovations to offer a quicker, smoother, and reliable experience to the lenders and borrowers. There are lots of challenges faced by the industry as of now. Developing better machine learning algorithms might be the key to solving them all.

The post How Machine Learning Will Transform P2P Lending appeared first on Lending Times.

Why Community Banks and Fintech Partnerships Make Cents

bank-fintech partnerships

Fintech is often viewed as an industry disrupter, but its greatest influence may be as a collaborator, benefiting both banks and themselves, especially in the lending space. Conceptually, partnering makes sense. For community banks, the cost of building or buying their own online origination platform is prohibitive. By collaborating, banks can achieve more with less […]

bank-fintech partnerships

Fintech is often viewed as an industry disrupter, but its greatest influence may be as a collaborator, benefiting both banks and themselves, especially in the lending space. Conceptually, partnering makes sense. For community banks, the cost of building or buying their own online origination platform is prohibitive. By collaborating, banks can achieve more with less risk: They can get improved services for significantly lower capital expenditure; a reduced cost of doing business; and, more importantly, access to market segments that would otherwise not meet their credit criteria. Collectively, this advances not only the business of community banks but also the mission.

In turn, by partnering with banks, fintech firms can gain brand exposure, more quickly scale their businesses, and increase their access to capital and liquidity, which translates to better company returns.

Bank and Fintech Partnership Models

Initially, bank-fintech partnerships followed consumer demand for digital services, especially mobile access. Lending partnerships soon followed, first focusing on retail consumers, and more recently, on SMEs. Partnership structures vary depending on which party sources the borrower, underwrites and funds each loan, and whether the product is white labeled under the bank’s name or co-branded. Below is an expanded chart of most common structures and selected partnerships, published by Lend Academy.

Borrower Source Underwriter Lender Abbrev. Customer Style Year Example (Bank/fintech)
Fintech Fintech Bank FFB Retail N/A 2014 Union Bank / Lending Club
Fintech Fintech Bank FFB SME Co-brand 2015 BancAlliance / Lending Club
Fintech Fintech Bank FFB Retail N/A 2016 Credigy / Lending Club
Fintech Fintech Bank BFB SME White label 2015 ING / Kabbage
Bank Fintech Bank BFB SME White label 2016 JPMorgan / OnDeck
Bank Fintech Bank BFB SME White label 2016 Santander / Kabbage
Bank Fintech Bank BFF Retail Co-brand 2015 Radius Bank / Prosper
Bank Fintech Fintech BFF SME Co-brand 2015 Regions Bank / Fundation
Bank Fintech Fintech BFF Retail Co-brand 2016 Regions Bank / Avant
Bank Fintech Bank/Fintech BF-BF SME Co-brand 2017 New Resource Bank / P2Binvestor

Partnership Model Breakdown

Lending Club, one of the industry’s largest online consumer lenders, partnered with a series of banks via the FFB model. In their case, the banks provided lending capital as part of Lending Club’s P2P investor base. This partnership type expands the bank’s loan portfolio and enhances the online lender’s access to capital.

JP Morgan’s partnership with OnDeck (BFB) improved the “difficult customer experience” of securing a small business loan by combining existing customer data with a streamlined underwriting platform to fund loans more quickly.

The BFF model uses the bank’s customer base to source borrowers while the fintech firm underwrites and funds the loan. Banks generally receive referral fees when their customers borrow via the fintech portal. With the Regions Bank and Fundation partnership, Fundation funds loans up to $1 million while Regions handles larger loans.

P2Binvestor and New Resource Bank advanced the bank-fintech partnership into new territory with a co-lending, asset-based financing product. Together, the bank and P2Bi provide the capital, and the bank sits in a senior lien position. P2Bi underwrites and manages each facility, essentially acting as an ABL service provider. In turn, borrowers receive a blended interest rate that reflects the risk profiles of both the bank and the fintech firm.

This model (BF-BF) offers several unique benefits for banks and borrowers. For banks, it provides the opportunity to support small businesses that would otherwise not meet their credit criteria and allows them to generate additional lending revenue with less risk. In addition, the co-lending structure acts as a potential onboarding mechanism to traditional bank lending as once the borrower qualifies it can graduate to the bank’s regular lending products. Since P2Binvestor’s technology is already integrated with the bank partner, the transition from bank-fintech partnership to bank is seamless, also a significant borrower benefit.

Challenges

There can, however, be challenges. In a recent Manatt survey on bank-fintech partnerships, bank respondents cited overall preparedness as a point of concern when considering a fintech collaboration. Per mandates from the Office of the Comptroller of the Currency (OCC) and Consumer Financial Protection Bureau (CFPB), banks must implement appropriate oversight and risk management processes for third-party relationships and service providers. Other issues for community banks include data security and staff training, and technology integration with legacy systems. Due to these concerns, it’s imperative that when in conversations with a fintech firm, community banks are clear about the responsibilities, requirements, and protections that will contribute toward a successful partnership.

Conclusion

As seen above, there are numerous ways that banks and fintechs can partner together in order to meet the needs of the consumer involved. Although challenges can be present, it’s important to address these issues before they surface in order to prevent them from happening. Overall, the ROI from these partnerships can ultimately change the success of a business.

Author:

Krista Morgan is CEO of P2Binvestors.

For more on community bank and fintech partnerships, check out P2Binvestor’s white paper on the topic.

Algorithmic Trading in Real Estate Investing

AlphaFlow

A secular growth trend in the real estate market and a growing US economy is expected to be a strong tailwind for real estate financing companies and should have a commensurable positive effect on asset management companies in the space. AlphaFlow, a California registered investment advisor, is amongst the first and fastest growing automated real […]

AlphaFlow

A secular growth trend in the real estate market and a growing US economy is expected to be a strong tailwind for real estate financing companies and should have a commensurable positive effect on asset management companies in the space. AlphaFlow, a California registered investment advisor, is amongst the first and fastest growing automated real estate investment services in the US. The company launched operations in 2015. Its first avatar was focused on bringing consolidated reporting, transparency, and insights to real-estate crowdfunding investments on multiple platforms. The company, under Founder and CEO Ray Sturm (formerly founder of RealtyShares), has now graduated to cutting-edge algorithmic investing.

Meet The New AlphaFlow

The company has raised a total of $6.4 million with $4.1 million coming in the latest funding round (September 2017). The round was led by Hedge Fund titan Steven Cohen’s Point72 Ventures while other marquee names likes Social Capital and Y Combinator also joined in the raise. The funds are being deployed to build infrastructure, develop underwriting resources, finance partnerships with leading online lenders & investors, and to update the company’s technology.

AlphaFlow launched the industry’s first fund allowing investors to diversify their portfolios through multiple real estate loans with an option to reinvest earnings automatically. Investors now have a true opportunity to diversify.

In March 2017, AlphaFlow launched an Automated Investment Platform incorporating SMA (Separately Managed Accounts) and artificial intelligence technology offering portfolio optimization and diversification services in a way that each client has his unique asset portfolio at minimal cost. This relieved clients from the hassle of managing their portfolios as the entire administration and monitoring is in the hands of experienced professionals.

The AlphaFlow Working Model

AlphaFlow is working on an asset management model and not a marketplace lending model. The company stands out from the crowd because its investments are spread across 75-100 loans aiming at net returns of 8-10% and targeting lower LTVs.

 

Source: AlphaFlow.com

Loans are purchased/underwritten from lending platforms after performing a detailed due diligence process. Each client’s portfolio is under automatic review on a daily basis to keep it diversified. Loan underwriting is not a fully automated process, however. The algorithm and allocation methodology used are proprietary while loans not fulfilling predefined criteria are rejected. This ensures that the fund is not on an auto-underwriting mode and every loan is analyzed to evaluate suitability for client’s portfolio.

The platform charges a fee of 1% on invested capital and its loans are usually for a tenure of 6-12 months. The minimum investment amount is $10,000 for each investor. AlphaFlow currently has over 280 investors on board.

Normally, loans are repaid earlier than the credit period sanctioned, where AlphaFlow’s automatic system re-balances the entire portfolio. Also, the portfolio of each client is re-balanced with the addition of new loans to the platform.

The Current Scenario

Sturm shares his thoughts on the real estate crowdfunding industry freely. “Most platforms are facing challenges related to managing customer acquisition costs (CAC),” he says, “which have started topping $5,000. Growing competition further fuels the CAC. Though VC funding has been strong in the segment, this has shifted the balance towards institutional investors as compared to originally empowering retail investors. The winner in the space will be the one who can control his CAC and develop an artificial intelligence-powered underwriting technology.”

With sufficient capital in hand, AlphaFlow is now looking for partnerships with online lenders to expand its business reach. Its criteria for selecting partners is to focus on their underwriting technology, reporting infrastructure, and transparency. Another challenge the industry as a whole is facing is that it has become harder to determine which lender is doing better. For an investor, it is fundamental to their job, but transparency has taken a hit in the current ecosystem.

Future Prospects

A changing economic environment will lead to shifting trends in the housing market, in terms of job growth, affordability, increased demand, and nominal interest rates. Credit models need to be able to stomach this shift. AlphaFlow is still building out its technology, but the solution developed has helped it lower delinquency rates by more than 80 percent as compared to the rest of the industry. Also, most platforms talk about the need to synthesize information using AI and machine learning, but few have done it well.

Real estate crowdfunding firms will need artificial intelligence to analyze all the data points on scale, and it will soon become uneconomical to hire a massive team to execute all these repetitive tasks. Moreover, profitability is not the function of growth alone. AI-empowered smart underwriting will be the key differentiator, Sturm believes. He shares that it is always easy to grow by underwriting bad deals, but it is suicide in the long run when capital deployed is not able to hit target returns or, even worse, if principal is destroyed during the process.

AlphaFlow became a Registered Investment Advisor to showcase to investors that it is on their side and that it has a fiduciary duty to protect their interests. Serious players in the segment will have to move in this direction to achieve scale in the asset management industry and better serve their customers.

Although AlphaFlow is doing well with its present underwriting model and clients are satisfied with the results, it is always searching for improvements that can be incorporated into the system. It is also considering multiple avenues to get a diversified exposure in the real estate industry. It might go for a combination of debt and equity deals for better exposure. Different duration debt deals may be targeted (2 or 3 years tenure) with a locked interest rate of 5 percent. To be a market-driven company, strategies will need to be reframed accordingly.

Integration of blockchain technology can transform the entire operational aspect of the real estate industry. But Sturm believes it is still a long way from maturing enough for real estate investing. The technology is currently not on his road map, but he might get interested if someone is able to introduce a breakthrough product.

Conclusion

To achieve scale and a strong position in the industry, AlphaFlow will keep investing in data science and engineering. In today’s fintech world, that is the only moat a business can cross.

Author:

Written by Heena Dhir.

Tuesday May 22 2018 Daily News Digest

GreenSky vs. LendingClub

News Comments Today’s main news: Kabbage to launch payment services. Funding Circle SME Income Fund limited force signal moves past key line. Zopa boosts TruFin results. DEPO launches to help lenders accept digital assets as collateral. Qudian stock drops 16.5%. Today’s main analysis: Deep dive into MFT 2018-2 vs. AVNT 2018-A (A MUST-READ). Today’s thought-provoking articles: Credit score improvement […]

GreenSky vs. LendingClub

News Comments

United States

United Kingdom

European Union

International

Other

News Summary

United States

Online lender Kabbage to launch payment services by year-end (Reuters) Rated: AAA

Kabbage Inc, a U.S. online lender for small businesses, plans to launch payment processing services by year-end, President Kathryn Petralia said on Monday, helping it to diversify and compete more directly with industry leaders PayPal Holdings Inc and Square Inc.

The Atlanta-based startup will offer tools to enable clients, mostly brick-and-mortar businesses, to accept card payments in-store and online, Petralia said in an interview.

Deal Deep Dive MFT 2018-2 vs AVNT 2018-A (PeerIQ), Rated: AAA

This week we compare 2 very different MPL personal loan securitizations – Marlette’s MFT 2018-2 Prime deal and Avant’s AVNT 2018-A Near Prime deal.

Collateral Comparison 

AVNT 2018-A has lower average loan size by $6,435, shorter weighted average loan terms by 9 months and higher WAC by 16.28%. This is a reflection of the quality of borrowers that Avant and Marlette target. Marlette’s prime borrowers have higher weighted average FICO scores by 59 points than Avant’s near prime borrowers. The geographic distribution is quite similar between the two deals.

Source: PeerIQ, KBRA

Bond Characteristics and Pricing

The significantly higher WAC on AVNT 2018-A leads to a 14.8% pickup in excess spread. KBRA’s base case loss estimate is 7.4% higher on AVNT 2018-A, which leads to a 7.4% higher loss-adjusted excess spread on AVNT 2018-A.

Source: PeerIQ, KBRA

Capital Structure

AVNT 2018-A has 3.3% lower O/C which is compensated by 14.8% higher excess spread. The A tranches have similar CE in both deals but Marlette’s A is rated one notch higher.

Source: PeerIQ

Avant continues to benefit from tighter underwriting criteria (Asset Securitization Report) Rated: A

The introduction of tighter underwriting criteria continues to pay off for the online consumer lender Avant.

The company, which was founded in 2012 and is based in Chicago, was able to lower the credit enhancement, again, on its latest securitization, the $221.9 million Avant Loans Funding Trust 2018-A.

Kroll Bond Rating Agency assigned an A- to the $149 million senior tranche of notes to be issued, which benefit from 38.42% credit enhancement. That’s down from 41.8% on the comparable tranche of its prior transaction, completed last year.

LendingTree Study Finds More than Half of My LendingTree Users Improved Credit Scores in Majority of 50 Metros Analyzed (PR Newswire) Rated: AAA

LendingTree today released its study on the top places with rising credit scores. With credit scores being a crucial component of personal financial stability and opportunity, LendingTree analysts decided to look at anonymized My LendingTree users who logged into their accounts in both the first quarter of 2017 and the first quarter of 2018 to determine the top metros for rising credit scores among the 50 largest in the United States.

Below are some of the key takeaways from the study.

  • JacksonvilleIndianapolisDenver and Tampa saw the highest rate of rising credit scores among the 50 biggest metros from Q1 2017 to Q1 2018.
  • Virginia Beach, Va.Los Angeles and Birmingham, Ala., had the lowest rate of rising credit scores, with 47 percent of Virginia Beach users raised their credit scores.
  • San Jose (Silicon Valley) saw the most dramatic rises in credit scores, with the highest rates of people who raised their score by more than 75 points and 100 points.
  • In the majority of the 50 metros analyzed, more than 50 percent of users improved their credit scores between Q1 2017 and Q1 2018.
  • About one in three increased their scores by over 20 points, and 3.5 percent were able to improve their scores by 100 points or more.
Source: Lending Tree
Source: Lending Tree

New Fintech IPO Offers Litmus Test for Online Lenders (Wall Street Journal) Rated: AAA

It wasn’t long ago that online lenders were ascendant. More than $3 billion in capital from investors as diverse as Japanese conglomerate SoftBank Group Corp. and celebrity chef David Chang gushed into lending startups in 2015, according to Dow Jones VentureSource. Analysts at Morgan Stanley predicted that year that the nascent industry would account for 10% of all unsecured consumer and small-business loans by 2020.

Investors soured on the sector. Shares of LendingClub, which once had a market value of about $10 billion, are down 77% from their IPO price. Prosper’s valuation was slashed by more than two-thirds in a private fundraising round last year.

Source: The Wall Street Journal

GreenSky said in its IPO filings that it has facilitated more than $12 billion in loans to consumers for home-improvement projects and elective medical procedures.

Part of GreenSky’s advantage comes from its relatively low customer-acquisition costs. LendingClub’s biggest expense is sales and marketing, which last year rose to $229.9 million, equivalent to 40% of revenue.

Source: The Wall Street Journal

 

How to responsibly invest in bitcoin (Bankrate) Rated: A

Recently, Bank of America, Chase, and Citigroup joined Capital One and Discover in banning cardholders from using them to buy cryptocurrencies. Credit cards were one of the most popular payment methods because of their relatively low fees and instant transaction rates, and investors are having to look at other options to make their investments.

Source: Bankrate

You can borrow money from a family member or friend, or you can use a peer-to-peer lending platform like SoFi to leverage funds for Bitcoin investments. However, be cautious when borrowing money for an investment. Interest rates can eliminate any gains you get from the investment, and the risk of losing money in such a volatile market is high.

Is Mulvaney targeting fintech or nonbanks? (Respa News) Rated: A

The acting director also responded to a question about qualified mortgages which has left the industry scratching its head since. Was Mulvaney separating fintech marketplace lending from traditional mortgage lending, or was he drawing a line between depository mortgage and non-depository mortgages?

 

Banking overhaul heading for likely passage in Congress (Yahoo! Finance) Rated: A

Legislation that would ease banking regulations — and modify rules governing credit reports and some consumer loans — is headed for likely passage in Congress any day now.

The bill cleared the Senate in March with some bipartisan support and is expected to be voted on by House lawmakers this week, perhaps as early as Tuesday.

The measure rolls back some of the regulations imposed by the Dodd-Frank Act of 2010. That legislation came on the heels of the financial meltdown that rocked the U.S. economy a decade ago, when risky and unaffordable mortgages contributed to millions of homeowners losing their houses to foreclosure.

Main Street Banks’ New Lending Rivals: Hedge Funds and Private Equity (Wall Street Journal) Rated: A

Main Street banks are feeling squeezed by competition from new rivals: nonbanks like hedge funds and private-equity firms that are elbowing into business loans.

Growth in business lending has picked up recently—it was up 3.3% year over year as of May 9, according to Federal Reserve data released Friday, after falling below 1% earlier this year. But the growth rate is still far below where it’s been in recent years, when loans to businesses grew at a double-digit clip for much of 2014, 2015 and 2016.

Making technology the solution, not the problem, at small banks (American Banker) Rated: A

The board members of R Bank in Round Rock, Texas — who include the Hall of Fame fireballer Nolan Ryan, a co-founder of the bank — hold accounts there, and they, like most other patrons, knew its old technology made for clunky customer service.

So, says president and CEO Steve Stapp, he channeled those irksome experiences into board support for an investment in a systems overhaul at the $455 million-asset bank.

For community banks in highly competitive markets, service with a personal touch can be a differentiator to win and keep customers. But when legacy technology hampers the customer experience, all the cups of coffee in the world won’t help.

 

Vota turns your credit card transactions into recommendations, helps you spot fraud (Tech Crunch) Rated: A

Blippy, which was hyped up to a $46.2 million valuation back in 2010 before the world realized that almost nobody wanted a dedicated network for sharing and viewing each others’ purchases. Well, guess what? Someone’s trying a Blippy-like thing again — this time, in the form of a new app called Vota, which automatically records your credit card purchases and the places you visit so you can share them with friends or family, or view them privately for your own reference.

As a byproduct of this data collection, you may spot credit card fraud or other errant charges, too, or just get a handle on your spending.

Optimal Blue First-to-Market with Pipeline & Lock Management APIs (Business Wire) Rated: B

Optimal Blue is proud to recognize enterprise SaaS digital mortgage solution leader, Capsilon, as its first strategic partner to complete certification with the highly anticipated Pipeline & Lock Management APIs. By debuting these innovative system-to-system API interfaces in the mortgage industry, Optimal Blue has enabled Capsilon’s digital mortgage platform to fully support the creation, management, registration, and locking of first-lien mortgages instantaneously with Optimal Blue. As a result of this advanced integration, a completed application and pre-approval are done in half the time of the traditional back-and-forth processes, empowering loan officers to be more competitive in today’s purchase market and win more business from real estate agents.

Mastercard Unveils Fintech Initiative Aimed at Digital Banking (Banker & Tadesman) Rated: B

The company on Monday announced the creation of Accelerate, a new initiative to drive growth at scale for the fast-evolving fintech industry, reflecting the company’s ongoing commitment to this sector.

Designed to operate alongside its successful Start Path program, Accelerate will broaden Mastercard’s engagement with the payment fintech community including the next generation of digital banks.

United Kingdom

Funding Circle Sme Income Fund Limited Force Signal Moves Past Key Line  (Concordia Review) Rated: AAA

Checking on current RSI levels on shares of Funding Circle Sme Income Fund Limited (FCIF.L), the 14-day RSI is currently standing at 58.31, the 7-day is at 65.97, and the 3-day is resting at 83.22.

Funding Circle Sme Income Fund Limited (FCIF.L) currently has a 14-day Commodity Channel Index (CCI) of 148.41.

Shares of Funding Circle Sme Income Fund Limited (FCIF.L) have a 200-day moving average of 103.52. The 50-day is 104.93, and the 7-day is sitting at 104.82.

 

Zopa stake boosts TruFin annual results (AltFi News) Rated: AAA

TruFin, the AIM listed fintech lender and payments provider, has released its first set of annual results following on from its public listing back in February. The numbers show a 7.67 per cent uptick in its valuation of its stake in p2p lender Zopa in 2017.

TruFin, which says it used an external company to aid the valuation of Zopa, re-valued its holding upwards by £2.6m to £36.5m over the course of the year. The firm, which was spun out of hedge fund Arrowgrass’ fintech holdings, holds a c.15 per cent stake in Zopa bought by Arrowgrass in 2014 for £15m. TruFin was set up by Henry Kenner, one of the founders of Arrowgrass, who is also its CEO and chairman. The hedge fund itself was launched by a group of Deutsche Bank traders in the wake of the financial crisis, including Kenner.

UK watchdog says automated financial advice falls short (Rueters) Rated: A

Advice doled out online or via smartphone apps, referred to in the industry as “robo advice”, aims to cut costs for customers looking to save or invest. It also seeks to foster innovation and increase competition in financial services.

But the Financial Conduct Authority (FCA) said two reviews of the industry uncovered problems among early entrants.

Exclusive: Former ADS Securities exec Jamieson Blake joins specialty lender Basset and Gold (Leaprate) Rated: B

Following our exclusive report from earlier this month that Jamieson Blake, Head of Client Experience at the FCA regulated London based arm of ADS Securities, had resigned from the company, LeapRate has now learned that Mr. Blake has landed – at specialty lending and retail investment firm Basset and Gold, as Head of Relationship Management.

China

Why Qudian Inc Stock Dropped 16.5% Today (Motley Fool) Rated: AAA

Shares of Qudian (NYSE:QD) closed down 16.5% on Monday after the Chinese online lender announced earnings that fell short of expectations.

Qudian reported “diluted adjusted net income per share” of $0.16 but GAAP diluted net income per share of only $0.15 per share. Whichever yardstick you use, though, these numbers appear to be lower than the $0.17-per-share estimate quoted on Yahoo! Finance. Revenue, on the other hand, came in at $273.7 million, significantly above consensus expectations for $214.6 million.

European Union

European Company Helps Turning Cryptocurrency into Collateral (the Merkle) Rated: AAA

Following a similar model as traditional depository services, DEPO gives lenders the freedom to accept digital assets as loan collateral. The platform also allows borrowers to keep ownership of their digital asset during the entire loan period.  The platform also protects future financial gain of the asset for borrowers with its decentralized design.

By employing the DEPO platform, lenders will be able to accept cryptocurrency as collateral for loans. To be protected, lenders can request additional collateral, or a partial sale of the asset should the market become excessively volatile at any time during the loan period.

Matthias Setzer of PayU (Lend Academy) Rated: A

In this podcast you will learn:

  • The history of Naspers, the parent company of PayU.
  • What PayU does and the markets where it operates.
  • Why Matthias decided to leave PayPal after 12 years and move to PayU.
  • How PayU approaches going into a new international market.
  • The Naspers investment in Chinese giant Tencent and the PayU footprint in China.
  • Why the number one country PayU is focused on today is India.
  • Why they invested €110 million in Kreditech and how they are leveraging that partnership.
  • The point of sale lending product they have launched in India with Kreditech.
  • The biggest growth drivers for PayU over the next 12-18 months.

New Insight will change the way you think about data (Instantor Email) Rated: A

Today Instantor, the Swedish fintech company making financial decisions easy, announces Insight. A new product that will transform the way financial organisations assess risk for loan applicants. By using robust machine learning, Insight analyses more than 70 predictive features and insightful patterns in historical banking, and can be used to make better risk and opportunity decisions. Instead of having a risk team spending months testing risk models, Insight ́s intelligent features will build the most optimal risk model using the clients own data and can be up and running within a week.

Bricknode and Lendytech unite under Untie Group banner (Finextra) Rated: B

Untie Group used to be several companies, the largest of which were Bricknode and Lendytech. They had a common founder in Stefan Willebrand and used, at least to a degree, the same self developed software. Also a number of people have gone from one firm to the other over the years.

International

Decentralized Lending Promises Easy And Global Access To Credit, But Is It Too Good To Be True? (Forbes) Rated: AAA

Since the rise of cryptocurrencies, the term “decentralized” seems to be everywhere. Decentralization has been proposed in many industries as a way to heighten transparency and make transactions simpler. One field in particular which has shown great potential for the application of decentralization is money lending. As many might rightly ask, don’t we need banks who are willing to take the financial risk and approve loans? As it turns out, maybe we don’t.

For lenders, the use of smart contracts allows for much easier assessments of the counter-party’s trustworthiness. Something that would take traditional audits weeks, not to mention the costs of such a traditional audit. Validating transactions and follow-up can become fully automated. Collateral is automatically returned at the end of the loan period or liquidated if the loan is defaulted on, removing many of the time-consuming actions that come with it.

Automation, ‘platformisation’ tipped to take hold in banking (AltFi News) Rated: A

The report, entitled Whose customer are you? The reality of digital banking, shows that 73 per cent of bankers believe retail banking will be at least 80 per cent automated in the next two years. A further 78 per cent see ‘platformisation’ steering the market in the future.

71 per cent of respondents are focusing their digital investment budget on cyber security, up from 34 per cent last year. Yet a mere 17 per cent are thinking about the risks of third-party integrations under Open Banking.

Fin-tech changes the loan process: Meet new lending models (Bankless Times) Rated: A

The new FinTech lending model opens new opportunities to people who were not able to borrow from traditional banks and other financial institutions because of the poor credit history and other factors. Such loans are now available to the new groups of people who need an instant funding, for instance, small business owners, students etc. In particular, entrepreneurs got a chance to get a loan without collateral, which a while ago was a real obstacle for many business owners.

Millennials Choose FinTech

Millenials are the first generation to accept the real advantages of new technologies and ready to use them for their own convenience. When it comes to lending process, millennials no longer wish to visit bank branches personally, stand in lines, deal with unnecessary paperwork and wait for months to get the approval.

End of the Line…

Today we are already witnessing a drastic change in the lending model that existed for centuries. Consumers want to have a more flexible way to lend money but most importantly, they want this process to be quick. The FinTech industry already gave us this opportunity and hopefully, the following changes will be for the better.

 

Asia

Kieran Arasaratnam to Join Credify Founding Team as CFO (Digital Journal) Rated: B

Credify Inc., a pioneer in decentralised reputation systems, is pleased to announce that Kieran Arasaratnam is joining the founding team as Chief Financial Officer.

Authors:

George Popescu
Allen Taylor