A Discussion About Internet 3.0 , Decentralized Everything

Internet 3.0

Internet 1.0 is HTML websites. Internet 2.0 is a social network and user-created content. How is Internet 3.0 coming along? What is Internet 3.0? Are you familiar with Napster, Kazaa, and BitTorrent? Today, Bittorent has met Bitcoin and given birth to the following startups, networks, or organizations: Decentralized computing power. Golem, among others, is a […]

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Internet 3.0

Internet 1.0 is HTML websites.
Internet 2.0 is a social network and user-created content.
How is Internet 3.0 coming along?

What is Internet 3.0?

Are you familiar with Napster, Kazaa, and BitTorrent? Today, Bittorent has met Bitcoin and given birth to the following startups, networks, or organizations:

  • Decentralized computing power. Golem, among others, is a peer-to-peer market for putting your computer’s excess CPU power to use for other people. It works because there is no easy way to pay anybody on the planet fractions of a dollar for having used their CPU for 1 minute. This is, however, possible via blockchain.
  • Decentralized exchanges. Ether Delta, among others, is a cryptocurrency exchange which operates in a decentralized way (i.e., without a central counterparty). Decentralized exchanges allow peer-to-peer trading, which means that when a trade is executed the items are exchanged directly between the traders without touching any third party, and without the traders being able to stop the exchange. This approach eliminates counter-party risk entirely. On the other side, it also allows people to trade completely anonymously.
  • Decentralized protocol approval. Tezos, among others, is an open-source platform for assets and applications and allows the participants to vote to change its rules and protocols. Participants can choose to change the fee structure, rules, the protocol APIs, nearly everything. This protocol change-mechanism is built within the network rules, and nobody has the right of veto or override. Imagine if eBay merchants could vote to reduce the eBay fees without the eBay management being able to stop it. Of course, this opens the doors to politics, and also to oligarchies as having more Tezos coins obviously gives you more power to influence the votes.

Other similar companies include but are not limited to:

  • Decentralized file storage (Filecoin)
  • Decentralized domain naming (Namecoin)
  • Decentralized cloud storage (Storj)
  • Decentralized databases (BigchainDB, IPFS)
  • Decentralized internet address allocation (JACS)
  • Decentralized Video Encoding and Streaming (Livepeer).
  • Decentralize financial services (Bitcoin, Litecoin, etc.) and more.
Fig. 1. A map of some of the decentralized space from here.

Business models

Centralized online marketplaces , like Amazon, Uber, E-Bay or Lending Club, typically earn roughly 10%-35% of the value exchanges through the platform.

Other online platforms like Facebook or Google don’t share any of the ad revenue earned from the personal-data exchanged through the platform. They keep 100%.

In addition, all centralized marketplaces and platforms exert full control over who can advertise, who and what can be sold, to whom, where, etc.

Their full control, when the company is young or fragile, is not being exercised much. They want to attract users and customers. However, as the company grows, and pressure from investors and the financial markets increases, the platform position of the de facto monopoly in their sector is usually leveraged to increase fees and to control who and what can be transacted on the platform. For example, Google has a history of banning certain ad categories on its platform. Most people agree that the bans, so far, have been legitimate and are targeting harmful or mostly fraudulent industries from selling their products and services. However, Google’s power of life-or-death over entire industries is troublesome.

In comparison, decentralized networks and organizations have so far mostly tried a few different business models.

Financing and crypto coins

Traditional , centralized, startups sell their equity to investors. Equity is scarce by definition, to 100%. And once sold, investors typically have a contractual right preventing startups from creating more shares and diluting them without their approval.

Equity is a problem in a decentralized project. Equity to what? What does an equity holder control?

Most decentralized organizations mentioned above have created their own crypto coins in order to finance their creation. Their usual business model is to make the coin, artificially or legitimately, a required part of each transaction on their network. As the number of transactions grows and the coin inventory is limited, the coins become more valuable. And the network itself uses its own inventory of coins to finance its expenses. In addition, some decentralized networks also take a percentage of the value exchanged on their platforms.

However, the token approach has, so far, failed to work for most networks.

The most successful tokens today have thousands of active daily addresses.

Fig. 2 : Number of active addresses per network per day, log scale, for MakerDAO ( purple) , Tezos (blue), Binance (orange).

This is not surprising. All these decentralized organizations are new startups. It takes time for startups to build traction. A handful of them will have millions of users after 3-5 years. Most startups may still be viable businesses even though they only have hundreds of daily active users, but their tokens will not have any real value due to over-inventory. Therefore, maybe relying on token activity and scarcity to finance all decentralized projects may not be a viable way to finance these projects.

I believe an alternative token model is needed for most of these projects. A model that will have significant return to investors even if the network only achieves modest success of 100s of transactions per day. However, this may require an increase in network fees.

The X Open questions of decentralized entities

As I think of decentralization, many questions are on my  mind:

  1. What are these entities? Are they businesses, networks, organizations, protocols, or something else? The concept of Decentralized Autonomous Organization, or DAO, has been used in the past. But to my knowledge, no actively operating entity using a real DAO model is live and generating revenue today. All entities have executives, employees, bank accounts, offices, etc. Or is it? The Bitcoin network itself, with all the developers in various organizations who are trying to contribute to it, is fairly decentralized.
  2. Governance: Leaders in centralized entities are required. Often, leaders aren’t any good at taking decisions, but making some decision is often better than not being able to make any decision. Many an organization has died because nothing at all was done. Are decentralized organizations able to make decisions fast and efficiently over 5 to 10 years while they grow?
  3. Are decentralized networks cheaper to run, and do they have a disruptor advantage over centralized networks? It is not clear. Lending Club, one of the first P2P lending startups, argued that their cost structure was cheaper than banks’. However, it turns out the cost of capital lending and cost of customer acquisition were under-estimated and banks have cheaper capital and cheaper customer acquisition. Lending Club’s profit margins are not impressive. Neither is Uber’s. Nor are Amazon’s. I believe there is no single answer to this question, but assuming that a decentralized entity is more cost effective than a centralized entity is not obvious. In human history, disciplined centralized organizations (armies, empires, …) have clearly been more successful than federations, communes, etc.
  4. Is there value built, and where is it? The startup/VC model has worked since the Dot Com boom because it was a profitable model for everybody involved. VCs made money, and successful entrepreneurs attracted more smart wannabe entrepreneurs. It is very important to see the founders and investors in these decentralized organizations be successful or there will be no second generation decentralized entities.

Conclusion

What is the innovation here?

I believe that an exchange that can work without counterparty risk is a real innovation.

I believe that a method to pay fractions of a dollars efficiently to anybody on the planet is a real innovation.

I believe one day we will see the Netflix of Internet 3.0 bankrupt the Blockbuster of Internet 0, 1.0, or 2.0.

However, questions remain. Is decentralization in business similar to communism in politics? Does this model really work? In 1990, in Moscow, everything was rationed, bread was extremely scarce. When a communist leader asked the London mayor who is in charge of the bread supply to London so they can learn their secrets, the mayor, confused, answered “Nobody!” Our modern food supply is a decentralized market, and fewer and fewer people are going hungry.

Author:

George Popescu

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Digital Lending: How Artificial Intelligence, Machine Learning Are Making a Difference

machine learning digital lending

Artificial intelligence (AI) and machine learning (ML) are ubiquitous in today’s workplace conversations. Turn on any business news channel and you’ll hear them repeated over and over. Ask any venture capitalist and they are sure to brag about several investments in these areas. Google artificial intelligence and machine learning, and you’ll find 213,000,000 hits, and […]

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machine learning digital lending

Artificial intelligence (AI) and machine learning (ML) are ubiquitous in today’s workplace conversations. Turn on any business news channel and you’ll hear them repeated over and over. Ask any venture capitalist and they are sure to brag about several investments in these areas. Google artificial intelligence and machine learning, and you’ll find 213,000,000 hits, and rising. Overhyped? We don’t think so.

Accenture boldly claimed that AI could boost average profitability rates by 38% and lead to an economic benefit of $14 trillion by 2035. That is no small statement. Even more astonishing is the general alignment among analysts on this issue. It’s widely agreed that AI and ML hold great promise across all industries, and, specifically, in finance.

In 2019, IDC projected that banking would be the second largest global industry to invest in AI, with $5.6 billion going toward AI-enabled solutions (trailing only retail). Why? The anticipated effect on business. According to the research firm, Autonomous, the financial industry’s slice of the global AI pie represents upwards of $1 trillion in projected cost savings.

Fintech Disruptors and Underwriting

Fintech disruptors, characterized as fast-moving companies, often start-ups, focus on a particular web-based innovative financial technology or process, spanning mobile payments to lending. Fintech disruptors initially found an entry point in finance through the use of AI/ML in underwriting.

In the U.S., if the customer consents, you can gain almost unlimited data about their credit profile: how many loans they have, whether they have a mortgage, if they’re delinquent, and whether they requested credit recently. According to the Brookings Institution, “AI coupled with ML and big data, allows for far larger types of data to be factored into a credit calculation. Examples range from social media profiles, to what type of computer you are using, to what you wear, and where you buy your clothes.” Access to this type of data gave rise to the development of sophisticated algorithms to underwrite consumer credit risk. We’ve seen this across a variety of lending companies offering unsecured consumer, student, or even small business loans, particularly focused on digital lending.

Importantly, though, those employing AI must be hyperaware of data collection practices, model design, and the potential for misuse. There is an inherent obligation when using these powerful tools to avoid profit at any cost. When used responsibly, AI can promote growth and better serve consumers. To meet this goal, companies must focus on creating ecosystems that are exponentially more just and equitable than what we have today.

On the surface, the digital lending numbers seem incredible. Digital lenders have grown to $50 billion in originations per year, not including incumbents. And, the research firm Autonomous notes that the digital lender model continues to raise $5 billion in annual venture capital investment, dominated by investments in the U.S.

And, yet, that same report shows that an AI/ML-driven digitization of the lending process is not headed to zero cost. To date, the cost advantages of onboarding and ongoing servicing (up to 70% reductions) have not been able to overcome the relatively high marketing costs that have yet to effectively scale lower than $250 per loan. Moreover, capital costs can reduce efficacy relative to traditional bank competition, and, then, there are the unplanned expenses, such as legal fees or elevated product development costs, the firm reports.

So, if digital lending driven by AI/ML-powered underwriting cannot deliver a material cost advantage, is further AI/ML advancement possible? And, will it improve outcomes for the consumer? Yes, absolutely. It all boils down to operations. As the use of AI shifts beyond obvious use cases and is deployed cross-functionally across entire companies to address various operational inefficiencies, the real promise emerges.

AI/ML 2.0: Improving Outcomes for Everyone

According to Deloitte, the top 30% of financial services firms who are frontrunners are more adept at integrating AI into the core strategic business of their firms, delivering revenue and cost gains quicker than competitors. In our opinion, this is clearly the case with fintech disruptors. Those that are focused on AI integration throughout the organization will quickly pull ahead of those who limit AI deployments to chatbots, underwriting, and other AI/ML 1.0 use cases.

Fintech disruptors can offer the market’s most cost-effective solutions by dramatically curtailing operation costs. Harnessing large-scale, multi-functional AI systems across organizations, instead of simply deploying in underwriting, presents fintech disruptors the opportunity to control costs at each stage and offer quality outcomes for their customers at reduced costs – with lean workforces.

So, while these systems may not face the end customer in any way – in fact, that may not be visible at all – they are the true future of AI/ML for fintech disruptors.

Fintech disruptor leaders who understand the opportunity to use an interconnected system of AI models across their organizations will likely drive the greatest overall efficiencies, both reducing costs and boosting revenues. This enhanced efficiency can be used to drive competitive position and ultimately higher profits.

AI/ML 2.0 at Work

AI can be used to help allocate resources across a variety of functions. For instance, a lender could create an AI model used to predict which of its retail partners would see the greatest increase in usage as a result of a field visit by a partner support representative. Generally, these visits don’t have uniform outcomes. Therefore, using a model-driven approach could help to allocate resources in the most effective manner. Increasing usage obviously drives overall revenue, but also helps to amortize cost over a greater number of transactions, driving better unit economics. Further, with time, the usefulness of such a system can grow. The more data collected from previous visits, the better the algorithm can be at predicting which visits will yield increasing usage.

Or, a lender could deploy AI in the call center to optimize the efficiency of the collections support team. Outbound reach to delinquent customers could be prioritized based on an ML algorithm that evaluates the potential for a successful call and the expected dollar collection. This may sound simple, but making the “good” calls and avoiding the “bad” ones offers all the obvious advantages of more precise resource allocation.

What is less obvious, though, is how these models are interconnected. The model used in the call center complements the underwriting model. If the collections team performs better, then the underwriting model can be recalibrated to maintain the overall risk of the loan portfolio. If the model prioritizing field visits is working, then it increases usage and reduces the average costs to originate a loan. This further enables a recalibration of both the underwriting model and the collections model. The combination of these models, ultimately, increases both expected and realized returns on the loan portfolio, reducing expenses and allowing the company to pass this savings back to customers in the form of lower rates. This is a win for everyone.

Optimizing the AI/ML Ecosystem

This is the true promise of AI/ML – a robust ecosystem of interdependent models utilized to enhance cross-functional outcomes. This leads to a much broader point: inefficiencies exist in all aspects of business – including accounting, legal, operations, finance and customer experience – and negatively impact profits.

Responsibly managed AI/ML 2.0 promises to address many of these functional silos with great success, improving outcomes for everyone involved.

Author:

Dr. Tamir Hazan is a co-founder and head of Analytics at Digital Lending: How Artificial Intelligence, Machine Learning Are Making a Difference appeared first on Lending Times.

4 AI Underwriting Myths Debunked

data science

In 2017, HES designed GiniMachine, an AutoML platform for credit scoring. Since then, we have conducted more than 100 pilot projects for banks, credit unions, and financial companies. What follows is an overview of the top four misconceptions that bank managers have when approaching AI-based business solutions. An Unfathomable Volume of Data It is estimated […]

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data science

In 2017, HES designed GiniMachine, an AutoML platform for credit scoring. Since then, we have conducted more than 100 pilot projects for banks, credit unions, and financial companies.

What follows is an overview of the top four misconceptions that bank managers have when approaching AI-based business solutions.

An Unfathomable Volume of Data

It is estimated that humanity already produces 2.5 quintillion bytes of data per day, and by 2020, this amount will increase approximately 20 times according to a PwC Technology Report.

Needless to say, the sheer quantity of data is growing at an astounding rate. This increase in scale has led to a rise in entropy in the system, which in turn requires a huge number of tasks to be automated and properly managed. This is where AI comes in.

Machine learning allows us to analyze enormous quantities of information at a much greater speed than a human being ever could while also minimizing the risk of errors that so often occur when mere mortals do the work.

Myth #1: AI is Always a Black Box

The black box challenge has been argued over and debated for years.

When lending algorithms arrive at a final decision, quite often it’s unclear why. We know that a loan application is denied, but we don’t know if it was because of a payment history issue, because the applicant is young, or something else entirely.

This ambiguity is the core reason for the rise in demand for Explainable AI (XAI).

“The more accurate the algorithm, the harder it is to interpret, especially with deep learning networks,” points out Sameer Singh, assistant professor of computer science at the University of California, Irvine.

Today, different tools like feature and permutation importance, and methods like the SHARP method, can be used to make sense of black box AI models. For an in-depth look at the techniques for interpreting black box AI models, have a look at these two articles:

The easiest way to achieve interpretability is to use algorithms that create interpretable models, such as linear regression, logistic regression, tree-based models, rule-fits, Naive Bayes, and k-nearest neighbors.

Christoph Molnar’s book ‘Interpretable Machine Learning’ covers all the various algorithms.
AI-based dynamic systems are here to stay, however, the general public is highly unlikely to simply turn over their fate to an all-knowing black box system. Any institution that wants to retain its credibility will be forced to embrace XAI to make sure that it has the accountability and transparency necessary to maintain the public trust.

Myth #2: Datasets Must be Prepared by Data Scientists

As Andrew Ng points out in his Space Rocket Analogy, machine learning models that provide actionable results are fueled by data. Without data, the AI rocket just won’t fly.

The quality of the dataset will affect the quality of the predictive model. This means that machine learning algorithms require data to be prepared and formatted in a specific way.
Historical “raw” data is generally inaccurate as it may have missing values and outliers, or it might simply just have invalid data formats. That’s where a data scientist comes in.

The elements of data science pipeline

Data scientists usually engineer the data, spending weeks or months on this step. And data science is not a simple matter. It requires a blend of domain knowledge, math and statistics expertise, as well as code hacking skills.

But here’s the good news that nobody is telling you: You don’t need a data science team to get value from your data. The key to minimizing your data science needs is having the right technology: Technology that allows a data analyst to contribute in a meaningful way to data projects. Today, advanced ML platforms can perform perfectly with historical data without any preliminary analysis or data pre-processing.

Myth #3: AI Costs a Fortune

These days, AI is everywhere, and figuring out how to harness it is popular in any business.  Despite its ubiquity, many still believe that only massive enterprises have the budget to invest the supposed millions of dollars it takes to purchase AI-based technologies.

Why is AI now affordable?

  • As mentioned above, it is no longer necessary to hire a data scientist. The New York Times reported the cost of a “typical” AI specialist ranges from $300,000 to $500,000 a year between salary and stock options.
  • Ready-to-use developer tools are now widely available. In the past, AI-based solutions (i.e. Matlab, and SAS) were over-qualified and far too expensive. Now, feature-rich packages are available for Python — they offer almost identical functionality as their premium competition, but at a fraction of the cost.

The price of an AI-based system varies depending on the number of subsystems that a company chooses to implement. For example, if an enterprise needs a chatbot it can be as low as $15,000 depending on the complexity of the system. A subscription underwriting system for a SaaS platform can be purchased for only $1,000-$5,000 a month depending on the number of loan requests.

Myth #4: Risk Experts Don’t Need AI

The world is witnessing the biggest man-machine collaboration in history. The fear that AI will become smarter than humans and replace them is exaggerated in pop culture. Though some jobs will inevitably disappear, the remaining risk experts will find their work more fulfilling and stimulating than ever. It’s important to remember that AI is not the expert; it’s a tool for helping experts.

The benefits of AI for risk officers and data analysts.

  1. AI saves thousands of hours of manual processing, which means humans no longer need to perform tedious, repetitive tasks.
  2. At the enterprise level, AI means enhanced automation of administrative tasks, reduction of the overall workload, and a decrease in operational costs and labor.
  3. AI leads to better performance for risk experts. AI unlocks the ability to exploit hidden dependencies that are otherwise very difficult for humans to find on their own.

The purpose of AI is to provide a fast and secure backbone for risk management. But without experts, it’s nothing. It’s humans who train machines to perform tasks: Humans who understand the outcomes of those tasks, and humans who sustain the responsible use of machines for the benefit of other humans.

Redesign Risk Management

To get the most value from AI, companies must first discover and describe an operational area that can be improved. It might be a high churn rate, a balky credit scoring process, or half-powered historical data usage. AI can help to surface previously invisible problems and reduce current challenges.

Let’s not forget that issuing good loans is not just for enriching banks and lending institutions. Issuing loans helps recipients pursue their interests and enrich their lives. Risk officers have a duty to use all the tools at their disposal to this end, for the benefit of both parties.

Author:

Natalie Pavlovskaya is the chief marketing officer at HES (HiEnd Systems), a fintech company behind comprehensive lending and credit scoring solutions. She is a marketing executive with international business experience in CIS, EMEA, and US, working for more than seven years in digital marketing.

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Recent FinTech Woes Have Long-Term Public and Private Market Implications

Recent FinTech Woes Have Long-Term Public and Private Market Implications

With several fintech companies reporting earnings disappointments over the last two weeks and with FinTech Unicorn GreenSky, Inc. (GSKY) that IPO’d less than fifteen months ago now considering strategic alternatives, both public market and private equity investors have to consider the longer tail implications for the sector. Is the public market fairly valuing the underlying […]

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Recent FinTech Woes Have Long-Term Public and Private Market Implications

With several fintech companies reporting earnings disappointments over the last two weeks and with FinTech Unicorn GreenSky, Inc. (GSKY) that IPO’d less than fifteen months ago now considering strategic alternatives, both public market and private equity investors have to consider the longer tail implications for the sector. Is the public market fairly valuing the underlying business fundamentals and growth opportunities being pursued by fintech companies? Should GreenSky have stayed private? And given the GreenSky situation, will other FinTechs be able to gain access to much needed capital (both public and private) for further growth and value creation?

GreenSky has repeatedly under delivered relative to market expectations. The company reported second quarter earnings last week of $0.19 (versus consensus expectations of $0.23) due to weaker-than-expected lending volume. Not only were earnings short of expectations but transaction volumes grew 20% year-over-year when the company had indicated that it would deliver 30% growth. Add to these disappointing performance metrics the fact that one of GreenSky’s bank partners announced it would not renew its asset buying relationship with the company and GreenSky is now looking for alternative funding arrangements. All of this news was eclipsed by management announcing that it is considering “strategic alternatives”. The stock declined 37% on the day the quarter’s results were announced, driving its 21% underperformance for 2019 and adding to its 67% decline since its IPO just last May.

Public companies have nowhere to hide when things do not go as planned. When expectations are repeatedly not met and business models do not deliver the profits that technology-enabled business models suggest should be produced, recently minted public companies are punished severely. Public companies cannot contain the bad news to the board room where private equity investors complain loudly but their complaints lack the echo chamber of the public markets. GSKY cannot do a down round in private and has to take its lumps in public. There was similar news for OnDeck (ONDK) last week when it announced alongside its earnings that JPMorgan Chase (JPM) was ending its loan origination partnership (ONDK shares declined 23% that day and is down 43% YTD, and off 83% since its IPO). The FinTech world was banking on these bell weather companies delivering strong, sustainable business models that would reshape the financial services landscape. So far, this is not what happened.

GSKY’s and ONDK’s woes are only the beginning for private fintech companies. Earlier stage FinTechs look to the public FinTechs as reference points and hope that they can replicate their IPOs and deliver sustainable growth. GreenSky could have been that shining success, but it appears to be example of what can go wrong when a FinTech goes public. Growth stage companies tell their private investors that if they grow fast enough and big enough, they can go public — like public companies GSKY and ONDK. But, when these companies underperform with significant market value erosion and talk about exiting the public market, it sends shudders down the backs of private lenders and the investors who back them.

What are the lessons that private companies can glean from the disappointing news coming from the public FinTechs? Under promising is a winning strategy. Fin-techs have to stop falling into the trap of setting expectations so high that they miss delivering against them. The public market has demonstrated that newly minted public companies will be severely punished for missing performance targets. And while founders want big valuations and private equity investors need big write ups to be viewed as successful, they would both be far better served setting expectations lower by accepting lower initial public valuations and thereby allowing themselves to set lower performance targets for the 12 to 24 months after they IPO. This sort of thinking may seem logical to observers of this market, but when you are neck deep in the FinTech market as an operator, investor or banker, it is hard to avoid overheating expectations and valuation. With each capital raise leading to quantum leaps in valuations, private companies have to set very heady goals for their IPOs and for the year post going public. After all, the IPO buyers need a big return too. And this dynamic has ended recently with a very disillusioned FinTech market. If we wish to see FinTech deliver on the promise of next-generation financial services with transformational value add, better economics, and broad adoption, we have to give these companies the time to grow into world beaters. It cannot happen overnight and promising such only leads to creating a cynical market that will think twice about investing in early-stage and later-stage FinTech innovators.

Author:

Andrew Marquardt is a partner at Middlemarch Partners, LLC, a merchant banking firm that advises and invests in financial services companies, with a particular focus on fintech and tech-enabled growth companies.

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Loyalty and Fluidity in Alternative Financial Services and Traditional Lending

Growth of Funded Loan Volume Online

Non-prime lending has revolutionized the lending sector. In times where people lack a stable credit history, securing a traditional loan is not easy—and non-prime has become a go-to option in such scenarios. In the past few years, alternative financial services have gained momentum in terms of acceptability and volume. There are various companies in the […]

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Growth of Funded Loan Volume Online

Non-prime lending has revolutionized the lending sector. In times where people lack a stable credit history, securing a traditional loan is not easy—and non-prime has become a go-to option in such scenarios. In the past few years, alternative financial services have gained momentum in terms of acceptability and volume. There are various companies in the market that offer instant loans even to the borrowers who have a weak credit history. But how do we infer how many people have migrated to non-prime online borrowing from the traditional borrowing set up, and how many people have migrated back to the traditional set up?

Experian’s Clarity Services, a credit reporting agency specializing in near prime and subprime consumers, offers credit data to alternative financial service (AFS) providers. This helps lenders gain a wider perspective of non-prime applicants and further enables them to make more informed decisions.

The company furnishes the AFS trends report that specifies the prevailing trends and consumer behavior in the market by studying the underlying factors. In the 2019 AFS Lending Trends Report, Clarity studied a sample of 350 million consumer loan applications and more than 25 million loans to evaluate the market trends for the 2014 to 2018 time period. Clarity also leveraged Experian’s national credit bureau data to analyze consumer behavior.

Alternative Financial Services — What Do the Market Trends Say?

Non-prime consumers include people who may have been irresponsible with credit previously, youngsters with inadequate credit history, people who face sudden and unexpected emergencies, recent immigrants in the US or someone in immediate need of cash. The basis for the report includes factors of loan origination (involves the online and storefront channels) and loan types (includes installment payments and single pay).

In order to study the rise of the online lending market from 2014 to 2018, Clarity studied online installment and single pay loans by the number of loans originated and total dollars funded.

Growth of Funded Loan Volume ($) – Online

 

Growth of Funded Loan Volume ($) – Online Single

The graphs illustrate how online installment loans have been steadily growing from 2014 to 2018. The volume of online installment loans in 2018 was 7.4 times higher than the volume in 2014. Whereas, the volume grew up until 2016 in the case of online single pay loans, plummeted in 2017 and held steady in 2018.

As per the report, more than half of online borrowers are new to the alternative credit space. The table below illustrates the consumers who opened an online loan in 2018, tracking their past behavior from 2014 to 2018.

Clarity also tracked the activity of 2017 alternative financial borrowers in 2018 and if they continued with online platforms. The results showed that 41% of online borrowers again availed an alternative loan, while 24% of the borrowers did not show up in 2018. Also, 35% of the borrowers applied for a loan but did not open one.

Further investigations gave another interesting insight. Around 34% of 2017 borrowers who did not have any applications or loans in 2018 had switched to traditional lenders. This implies that 7% of overall 2017 borrowers migrated to traditional lending in 2018.

As per an examination of the credit classification of consumers who obtained and did not obtain loans from traditional lenders in 2018, 23% of borrowers who switched to traditional lending possessed a near prime credit score, and only 8% of the borrowers continuing in the alternative finance space were classified as near prime.

Factors Influencing Migration from Online Platforms to Traditional

While the migration of borrowers from AFS platforms to traditional ones might not be a shocker, borrowers who had a subprime credit score and were ineligible to apply for traditional loans were mostly the ones who moved to online or the AFS space to get the credit they needed. As and when their credit scores improved, they reverted to the traditional space. While AFS is convenient in terms of credit scores and repayments, there are strong factors that influence the borrowers to move back to traditional methods.

Frauds: With the advent of technology, fraud too has evolved. With data breaches, the fraudsters create a synthetic identity that cannot be easily decoded. This is leveraged by fraudsters to open fake and additional accounts.

Generation Bias: Gen X is more comfortable with online borrowing and less likely to be inclined towards storefront options. Another study under the report implies that the Silent and Boomer generations only account for 25% to 30% of all AFS borrowers.

Income Trends: In the past five years, online installment borrowers reported a higher income (while the values have been steady since 2016) and the reported incomes of storefront installment borrowers have been stagnant since 2014.

Conclusion

Due to the recession in 2008, the majority of borrowers had suffered a hit to their credit worthiness. On the other side, traditional lenders folded due to the toxic asset built up in their balance sheets. This created a vacuum for the AFS players to capture. It was a win-win as they were able to tap into a multi-hundred-billion-dollar market unchallenged, and the affected borrowers got a chance to get the credit they needed desperately.

With record economic growth, the 2019 scenario is different. Borrowers are returning to traditional ways of borrowing. The trends report puts light on the activities of the borrowers and how their needs have changed over time. In the given scenario, Clarity’s alternative credit data is a key asset when studying borrower behavior in the market.

Download the complete 2019 Alternative Financial Services Lending Trends report on Clarity’s website.

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Open Source Origination Technology Platform for Online Lenders

DigiFi lending solutions

Introduction Online lenders are fast becoming the first port of call to avail loans and have been attracting strong funding interest from VCs and PEs. This demand for a digital lending experience has also forced traditional lenders like banks and credit unions to figure out the technology which will allow them to originate loans in […]

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DigiFi lending solutions

Introduction

Online lenders are fast becoming the first port of call to avail loans and have been attracting strong funding interest from VCs and PEs. This demand for a digital lending experience has also forced traditional lenders like banks and credit unions to figure out the technology which will allow them to originate loans in a flexible yet scaleable way. They have two options: Buy or Build.

The build option can be extremely expensive and time consuming. But the buy option leads to a digital experience that is constrained, as you are dependent the features and functionalities of the vendor. Moreover, there is no way to really differentiate in the eyes of the digital customer. The solution is DigiFi: an open source tech platform which also allows you to customize along with a layer of additional services like hosting, support, platform implementation, etc.

DigiFI

DigiFi was founded by Joshua Jersey and Bradley Vanderstarren in 2014. It started its life as Promise Financial, an online lender, and raised $110 million in credit capital. It built up its own proprietary tech as there was no solution provider in 2014 offering an end-to-end loan origination platform that could automate the entire process. They sold off the tech to a large lending institution in 2017 and pivoted to DigiFi, one of the world’s first open source loan origination systems (LOS) which equips the lenders with flexible and modern tools to create unique platforms and digital experiences.

The company’s ideology is simple: That is to give other incumbent lenders, branches, credit unions, and startup digital lenders a platform where they do not struggle to build core lending capabilities from scratch. The company utilized the year 2017 and early 2018 to build up its platform, and started working with clients in late 2018. The company, with 10 people, has raised $4 million in equity to date and is based in New York.

The Market’s Pain Points and the DigiFi Solution

The ‘build or buy’ question creates a space for a platform that can bring together the qualities that fulfill the core origination requirements of the lending market and yet customize to give the client a competitive edge over other players. DigiFi empowers its clients to control the features and UI/UX so that it suits the specific needs of their unique client base. The existing tech vendors force the lenders into a rigid structure that limits flexibility to differentiate and provides the exact same experience for all sets of clients.

DigiFi gives the best of ‘buy vs. build’. Thus, DigiFi clients do not need to start from scratch and yet have the power to tailor the tech (buy and build, a win-win!). The company’s core platform is open source, and the source code can be accessed on Github. Revenue is generated from acting as a layer that provides hosting, support, platform implementation and customization services.

In crux, the platform of the company has features like complete lending CRM, decision engine for lending decisions, machine learning environment, and open-API architecture, and it can be configured for deployment across a range of lending verticals that include consumer, mortgage, small business, and commercial. DigiFi gives out the open source platform and its documentation for free.

The platform of the company is currently being leveraged by Sprout Mortgage, Mariner Finance, Constant Energy Capital, Greenwave, and Home Point Financial.

The Platform in Detail

The company provides its platform to the lenders for free and charges for additional services of configuration, setup, support, and running. Depending on the requirements of the client, DigiFi offers support plans for a monthly fee. The customization and platform implementation are charged on an hourly basis. The implementation time and cost varies. The implementation might take up to 4-8 weeks at a minimum and can take up to months if the lender needs to build out features from scratch. As compared to years and millions of dollars for building an in–house model, the DigiFi solution is usually in the 5-6 figure range.

The company’s platform is built on the JavaScript tech stack, and uses two well-established coding languages that are uncomplicated for clients to work with and engage. DigiFi focuses on end-to-end loan origination and concludes with onboarding onto a servicing system after funding.

The Future

As per the CEO of DigiFI, the incumbents are getting better with time as they have a lower cost of capital and existing customer base, positioning them to succeed. Getting the right tech partner on board is thus the critical piece to build a successful moat.

DigiFi offers a platform to lenders looking to tap the online lending market that not only equips them to get the best of the ‘buy vs. build’ system but also ensures full support and customization. It powers the lender with ready-made solutions, fast implementation, support and training, feature controls, unique customizations, flexible hosting options, and a contributor community. It provides the option to integrate all major data sources – Transunion, Equifax, Experian, MicroBilt, LexisNexis, etc. With over 45,000 development hours, DigiFi platform provides it clients a strong barrier to entry with complete configurability with other APIs, true scaleability with AWS, and integrated AI ML solutions.

Author:

Written by Heena Dhir.

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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.

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How QuantmRE Tokenizes Real Estate on the Blockchain

blockchain real estate eqre

Though the 2008-09 crisis scarred a lot of real estate investors, the fact is that the US real estate sector has been on a secular growth spree in this decade. The sector plays an integral role in the US economy and contributed $1.15 trillion to the country’s economic output in 2018, which is 6.2% of the nation’s […]

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blockchain real estate eqre

Though the 2008-09 crisis scarred a lot of real estate investors, the fact is that the US real estate sector has been on a secular growth spree in this decade. The sector plays an integral role in the US economy and contributed $1.15 trillion to the country’s economic output in 2018, which is 6.2% of the nation’s GDP. As a matter of fact, till Q3 2018, individuals owned US$25.6 trillion in real estate, and total mortgages amounted to US$10.3 trillion, which implies that home equity stood at US$15.2 trillion until Q3 2018. That’s a record in itself. Approximately 14.7 million homes have 50% or more of their total equity in their houses.

This represents a massive untapped financial asset for American homeowners. But wary of piling on more debt and with selling the house not an option, homeowners need a new solution to monetize their homes. QuantmRE has brought to market a win-win framework with its shared equity contract agreements. In conversation with Matthew Sullivan, founder/CEO of the company, Lending Times gets a front seat view on how the blockchain-powered solution will help millions of homeowners generate additional liquidity without taking on debt.

The QuantmRE Solution

The fundamental concept behind the company’s platform is “the home owner has equity and wants cash, whereas, the investor has money and wants equity.” QuantmRE’s solutions enable an individual to realize the value of their home’s equity without incurring any further debt. QuantmRE’s funds will purchase a fraction of the owner’s home equity and then tokenize it, building a marketplace for both owners and investors, with new financial opportunities. How?

The explanation lies ahead.

Structure of the Contracts

The QuantmRE contracts are flexible to cater to the needs of different stakeholders and are built to suit varied interests of the homeowners – with some contracts having a 10-year commitment attached to them and some contracts committing for 20-30 years. Along with this, the equity ownership framework will rely on risk profiles of the different contracts.

To illustrate, when the owner wants to part with 10% of the current value of their home, they are selling the future rights to its appreciation. When the owner sells the house in the future, they will provide the investor with a 15%-18% of the value of house. Thus, the company is buying 15%-18% of the future value of the owner’s house at 10% of the present value. This 10% is the shared equity. The company will cap the return in the initial years and the ROI will also be capped at 18%-20% per year.

These are strictly real estate contracts and not loans; here the homeowners simply agree to share the present or future appreciation or depreciation of the value of their real estate. The investor is investing in a real estate option and not investing in any debt.

Under the scenario where the house owner does not sell the house, the contract shall either be refinanced under the same terms as before or shall be renewed on new agreeable terms on all sides.

What are the returns for the investors?

Investor in the funds: The fund will buy the equity instruments from individual homeowners, and the investors will benefit from the overall diversification of the fund. The returns are usually asymmetric and are geared for strong positive alpha and downside protection (due to the structure where it pays for 10% on Day One but receives 15%-18% of future value). Even in the scenario where the house prices remain flat or fall marginally, the funds shall still deliver positive returns. But there is still risk involved, as the downside buffer is limited to an extent.

EQRE Tokens

The company is developing a cryptocurrency token with real intrinsic value: EQRE. These tokens shall be backed by audited pooled real estate assets; the tokens derive their value from the equity interests in the single-family owner-occupied homes. These tokens will be launched in the near future.

It provides a platform to the owners and investors to tap the previously illiquid real estate asset class in a stock market environment enabling the fractional interests in the real estate investments to be traded on the blockchain. The tokenization of real estate will allow small mom and pop investors to diversify their real estate holdings and provide institutional players a platform for liquidity in their real estate investments.

QuantmRE To Date

QuantmRE was founded in December 2017 and has successfully originated close to 100 transactions of over $25 million across a few states. Albeit, currently these transactions are not all QuantmRE contracts. Since its inception, the company has developed its technology, contracts, and design. and is just a few weeks away from launching its EQRE token.

As far as funding goes, the company raised close to US$2 million in 2018 in a seed round. The company plans to raise $5 million for working capital and expansion.

Conclusion

The company offers homeowners an opportunity to liquidate a part of their house without taking on more debt. Investors get to partake in the growth of single-family residential units. This is structured through a shared equity contract. These contracts will further back the token of the company that can be traded. The token of the company (EQRE) is being developed and shall be soon launched. QuantmRE plans to integrate the blockchain and cryptocurrency technology with the real estate sector allowing for liquid profitable real estate investments. The company has its eyes on the $15 trillion unmortgaged equity in US homes and is poised to leverage blockchain tech to make it a strong investment proposition.

Author:

Written by Heena Dhir.

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Launching an Online Lending Business: A Blueprint for Taking Off

consumer loans

Consumer lending in the US reached nearly 1.5 trillion dollars in 2018, according to the Federal Reserve Board of Governors, and European banks reported a demand growth of 25% in the second quarter of 2018. Needless to say, it’s a good time for lending. While banks are still paying out the lion’s share of the […]

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consumer loans

Consumer lending in the US reached nearly 1.5 trillion dollars in 2018, according to the Federal Reserve Board of Governors, and European banks reported a demand growth of 25% in the second quarter of 2018. Needless to say, it’s a good time for lending.

While banks are still paying out the lion’s share of the loans, alternative lenders are gradually moving in to fill holes in the ever-increasing lending market.

Fintechs Exploit the Growing Market

The traditional banking sector is entrenched in their old way of doing business. Banks and customers alike expect a certain customer experience and style of operational management. While this may appeal to some customers and lending institutions, it comes at a significant cost. Each teller or call-agent interaction at a traditional bank costs an average of four dollars compared to merely ten cents for a mobile interaction.

While the profits of running a fintech are clear, the process of getting up and running is not without its challenges.

Practical Steps for Setting up a Lending Company

Lending markets vary from country to country depending on regulations, legislation, and consumer behavior. This simple roadmap outlines the general process to get started.

Point One: Becoming A Legitimate Enterprise

In order to start lending online, business owners need to create a legal entity. This is the vehicle that all lenders will use to navigate red tape. As this process varies greatly from business to business, it may take as little as 1% or as much as 20% of your initial startup budget.

The basic steps include:

One possible way of circumventing this is to purchase an already existing bona fide legal entity or lending franchise. For example, the largest franchise lender in the US is Liquid Capital. The short-term costs may run a bit higher, however, the long-term benefits of using an existing household name could potentially pay large dividends.

In addition to the unique requirements for lending entities, regular business costs will often crop up as well. Among others, these could include hiring and administrative overhead and office rental. On average, these costs could take anywhere from 10%-12% of your startup budget.

Point Two: Raising Funds

Raising capital to lend out is the primary operational challenge for any lending startup.

Usually the best way for a startup to begin lending is with their own capital, but when that is not possible (or favorable) funds can be raised in the marketplace. Recently, institutional lenders have become much more comfortable with providing capital to lending startups following the rise of the P2P model.

Any lending company funded by public investors will have to factor in the cost of hiring a Certified Public Accounting firm to perform an audit to certify all financial data including their business plan, valuation, and other financials.

Point Three: Using the Right Technology Platform

The core of any modern lending company is the technological platform it runs on. The platform is the brain of the business and takes time to nurture and grow. It is best to do this in parallel with the other points as it is the primary capital asset of the operation.

When it comes to platforms, there are two main options: building your own from scratch or purchasing an existing platform from a vendor. This crucial decision will have a long-term impact on the business and will greatly affect setup and operational costs. Each option comes with pros and cons:

  • Building a lending system from scratch is more time-consuming, and can take up to 12 months. It requires a substantial upfront investment as you will need both financial and technological expertise to pull it off. Additionally, time-sensitive shifts in the market could be a factor, so timing your release is of paramount importance. While this option could be risky, it gives lenders full control over the product they build.
  • Purchasing an existing lending platform is generally less expensive and faster. There are a wide range of solutions both out-of-the-box and fully-customizable. The options fall into two general categories: traditional core banking systems (eg. Oracle, Temenos, and Infosys) or fintech-focused solutions (eg. HES Lending Software).

There are number of software challenges that digital lender should consider when choosing a platform:

  • In order to optimize productivity, systems often require further customization.
  • Some systems only cover a single or hand-full of loan management aspects like underwriting, loan origination, or loan servicing, and do not support many back-office functions.
  • Systems often do not integrate with the majority of third-party services, so lenders might end up needing to mix and match software to run their business.
  • Some systems do not extend well into new markets or product segments.
  • Some systems require license upgrades to increase the loan volume or number of user accounts.

In Conclusion

With a good understanding of the industry, thorough planning, and about $200,000 to $1,000,000 of startup capital, a state-of-the-art lending business can be launched. Not only do these businesses financially benefit their owners and investors, but they come with the satisfaction of knowing that every loan issued has great potential for improving the lives of the borrowers and their communities.

Author:

Natalie Pavlovskaya is the Chief Marketing Officer at HES (HiEnd Systems), a fintech company behind comprehensive lending and credit scoring solutions. She is a Marketing Executive with international business experience in CIS, EMEA, and US, working for more than 7 years in digital marketing.

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Why are so many payday lenders going into administration?

HCSTC loan volumes

“There will only be four main payday lenders operating in the sector.” This was the claim made by the Financial Conduct Authority (FCA) back in 2014, as I sat in a crowded seminar hall surrounded by other payday lenders and brokers. With the FCA taking over from the Office of Fair Trading that year, many […]

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HCSTC loan volumes

“There will only be four main payday lenders operating in the sector.”

This was the claim made by the Financial Conduct Authority (FCA) back in 2014, as I sat in a crowded seminar hall surrounded by other payday lenders and brokers. With the FCA taking over from the Office of Fair Trading that year, many industry players were expecting a shake-up as directors of payday loan companies and I huddled into this room trying to get some insight into the pending regulation.

Of course, we laughed off the idea of an industry with only four players. At this point, payday lending had been a booming business with a market valuation of £2 billion, over 3 million loans funded per year, around 200 lenders, and more than 200 brokers, easily. The industry was full of playboys on yachts, international millionaires, and soft regulation – how was it going to be changed so drastically?

Fast forward five years later and the controversial industry has changed dramatically with more and more lenders going into administration. The largest casualty has been market leader Wonga, who closed its books in Q4 last year, slowly followed by The Money Shop, Cash Genie, and recently Wageday Advance. But the question begs, how did these once formidable companies fall? And why are they going into administration?

Payday loans by numbers

Tougher regulation

In 2013, the payday loan industry was crying out for more regulation. The number of complaints was rising constantly, making headlines, attracting criticism from politicians such as Stella Creasy and religious figures such as Archbishop Justin Welby, and lenders were being accused of charging usurious rates as high as 5,000% APR.

On 1st January 2015, the FCA introduced a price cap on the amount that lenders could charge to 0.8% per day, meaning that, on average, a customer will repay a maximum of £124 per £100 and never repay double the amount they have asked to borrow. Other introductions included a maximum default charge of £15 per missed repayment and a strict authorisation process required for lenders and brokers to operate.

The initial costs and timescales of being authorised were too much for many brokers and lenders to handle with dozens leaving immediately, despite many being offered ‘interim permission.’

The introduction of a price cap, higher compliancy costs, and tougher regulation resulted in lower margins for lenders and a desire to run a stricter lending criteria to ensure maximum repayment.

Whilst many lenders have continued to trade, some have simply not been able to make the business model work – finding that the margins are too tight and the running costs are too high. For them, exiting the industry has been the safest option and, in 2019, we have only 40-50 payday lenders and a similar number of brokers.

High growth is catching up on them

Whilst the payday loan industry was booming pre-regulation, many lenders were issuing loans aggressively and growing exponentially. Wonga was notoriously cited for a £1 billion valuation.

However, this exponential growth came at the expense of issuing loans to customers that could not necessarily afford them, with soft affordability checks and funding based on more behavioural underwriting and aggressive collection practices than the traditional underwriting practices of credit checking and affordability.

The result? Millions of loans were funded to customers without employment, on benefits, no income, and no means of repaying their loan. Now, this group of debtors have a strong claim to ask for compensation, and this is now a thriving sector.

Compensation claims

With PPI claims coming to an end in August this year, the role of payday loan compensation claims is taking its place. Those who were issued a loan that they believed lacked checks are able to claim compensation of hundreds of pounds.

Wonga has been the lender most affected by this and has repaid over £200 million worth of compensation claims in the last four years – the process that has put them into administration.

Moreover, the cost of issuing a complaint demands a £500 fee from the Financial Ombudsman Service, regardless of whether it is a strong claim or not, which makes compensation claims a far greater expense.

There are a number of smaller, traditional payday lenders that have been around for over 10 years and were not lending big volumes prior to the FCA price cap – and these companies are now reaping the rewards. Companies such as Wizzcash, Uncle Buck, and MY JAR have the knowledge, resources, and financial competence to continue trading and thrive. As per the statistics below, there are 10 lenders that accounted for 85% of new loans – and as the number of lenders fall, the loan volumes are rising.

The future of payday lending

Payday lending will always have a role in the UK society. It is an important anti-poverty measure which offers a very important service to the 3 million people that apply for it every year – and its existence diminishes the risks of black market economies and loan sharking.

Whilst we initially laughed off the idea of only four payday lenders operating in the market, the rise in administration of well-known lenders is making this a real possibility.

Beyond payday loans, there is an opportunity for new alternatives to enter the market that can offer more flexible products including app-related banking, flexible overdrafts, and installment lending.

A flaw in payday lending is that all customers are subject to paying a high rate of interest, regardless of their credit rating. So those with average or good credit scores are still prone to paying the same high rates as those with bad credit ratings. If there is a lender that can find this balance, providing affordable payday loans for good credit and finding a way to accommodate bad credit customers, they will be able to crack a very complex market.

Author:

Written by Daniel Tannenbaum, co-founder of Tudor Lodge Consultants.

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