June 4, 2021
8 minutes to read
This story originally appeared on ValueWalk
Fintech’s Access Advantage: Alt Data and Analytics Open Credit Markets to New Consumers
Letters, conferences and more on hedge funds in the first quarter of 2021
A new economy needs a new approach to underwriting; fintechs are stepping in to fill in the pronounced gaps and rapidly increase market share along the way.
The conventional wisdom amid the recent backlash against all things tech is that AI and machine learning are inherently biased at worst, or at best only serve a segment of the population. Last year, for example, the Financial Services Committee working group held a hearing on ‘Fair AI’ in which Congressman Barry Loudermilk proposed: ‘There has to be a benchmark for compare the results of algorithms and assess the fairness of an algorithm’s decisions. The good news is that there is already a way to compare the fairness of fintech platforms; the bad news is that it exposes the prejudices of the status quo (ie the inexplicable “black box” that is the human mind).
Namely, research from New York University recently showed that among PPP lenders, fintech platforms as a whole were much more efficient than traditional banking institutions at providing loans to a population of borrowers. more diverse. For example, data showed that fintech lenders had a significantly larger share of their total number of loans made to black business owners than any other category of lenders, from small and medium-sized banks to large institutions. . Although the study focuses on business loans, it highlights the potential of fintech platforms to have the same impact on personal finances.
This is not to say that there is no room for improvement or that neural network technology has totally solved the problem of bias and access in consumer banking. There is indeed work to be done. But suggest that fintech platforms don’t have already Creating a better alternative for underserved communities or unclear credit profiles is to overlook the myriad ways in which technology is already making access easier for consumers.
The story of “efficiency” is universal for most digital solutions, although the historical bottlenecks that have limited consumer lending are likely more pronounced than anywhere else in finance. Automation, for example, allows lenders to process mortgages in days instead of weeks or months. A more intuitive user experience also eliminates the excessive change costs that have traditionally tipped the scales in favor of legacy institutions. If consumers are rejected or dislike the terms offered by their current banking relationships, the alternatives are just a few clicks away. Automation and speed, in this regard, already translates into more choice for consumers, more power to dictate improved conditions and better, more personalized results.
That said, it is the growth and adoption of alternative data and analytics that has the potential to have a much greater impact when it comes to access by giving decision makers infinitely deeper granularity and insight. more revealing about the creditworthiness or risks of potential borrowers. .
Addressing FICO’s blind spots
Few would argue that the technology does not deliver significant efficiencies for fintech lenders who underwrite consumer loans or for individuals who request them. Even Jamie Dimon, in JPMorgan Chase’s annual letter to shareholders, warned that a significant market share is at risk given the ability of fintechs to “integrate seamlessly with other platforms” and “use data intelligently ”.
However, upstarts don’t just target existing customers; they also integrate previously unbanked consumers with more dynamic underwriting models and personalized mass analytics that enable tailor-made lending solutions. When these platforms and applications gain critical mass, it will represent a radical transformation from the current state in which most potential borrowers are assessed on the basis of an accurate credit profile, marked by glaring deficiencies. ‘information.
When credit bureaus were first established in the 1970s, they helped democratize finance and provided structure where there was none before. Fifty years later, in a new economy, the blind spots of FICO scores are too obvious to ignore.
For the uninitiated, FICO is a statistical model used by credit issuers to predict a borrower’s probability of default. FICO’s rating is based on four main inputs – a borrower’s current credit, repayment history, defaults, and credit inquiries. Again, 50 years ago, FICO’s credit scores were revolutionary. They offered a traceable and somewhat objective measure to assess risk. And, make no mistake, an individual’s debt repayment history is indeed a good indicator of their ability to repay debts in the future.
However, fintech lenders today benefit from a more complete picture – thanks to data – that goes beyond traditional credit scores and reports. It is not just a question of subscribing with more precision and conviction; alternative data and new analytics allow fintech platforms to monitor their credit portfolios more diligently, while also expanding the scope of issuance without changing the risk profile of the credits they accept.
The benefits for borrowers are even more pronounced. Access to income and deposit data, for example, opens up funding to a whole range of individuals who represent low-risk loans, but struggle to adjust to the narrow archetype of creditworthiness. FICO. Information on exactly how people spend their money is also valuable and arguably much more relevant in measuring a borrower’s ability to repay a loan. Credit bureaus today can only access exits to pay off existing debt, which is a very small slice of an individual’s financial identity. This new granularity also allows fintech to offer hyper-personalized solutions in which the risk premiums correspond to the specific borrower and their unique financial situation and in real time.
The great concert opportunity
Consider how the economy has evolved over the past 50 years. Lenders just need a new approach to reframe the way they make their credit decisions – as risk models change, professions evolve, and employee-employer relationships become smoother. . The growing concert economy is a prime example.
More than a third of American workers are self-employed according to government estimates, and more than half of the Gen Z workforce is active in the concert economy. Depending on the specific area, gig workers often earn more than more traditional employees in comparable roles, especially in transportation, professional services and the health professions, according to ADP data comparing 1099-MISC employees. and traditional W-2 filers. Still, underwriting workers in the odd-job economy can be more difficult for lenders, given the “seasonality” of the specific business, barriers to employer verification, and other nuances.
Technology provides a tool for lenders to fill information gaps, and new entrants are indeed harnessing these capabilities to capture market share.
Jack LaMar, GTM Strategy Manager, New Products, at Plaid, noted in a recent webinar that when Uber and Lyft gained critical mass, neobanks were not far behind. He observed that a growing population has oriented their entire value proposition around one goal: “to serve employees in the odd-job economy.” That’s it.”
Credit has always been an indicator of confidence – in the economy, in ourselves and in our collective future. Individuals take out loans in pursuit of a vision. Lenders extend credit because they have faith – backed by data – that borrowers will be able to repay their debt.
Credit, as such, is the engine of the economy. But when people cannot access finance, whether due to unconscious biases, irregular income flows, or even a lack of credit history, it creates a handicap that leaves entire segments of the economy behind. population. It might sound like hyperbole, but many fintechs looking to disrupt the status quo do so because they recognize that there is a huge opportunity just by creating a level playing field. Access, they discover, does not imply an act of faith; it simply requires a different perspective, which is now available through alternative data and analysis.
About the Author
David Snitkof is a technology entrepreneur and data and analytics leader with a successful track record of building analytics systems, teams and businesses from the ground up. Most recently, he was head of data analytics and strategy at Kabbage, where he led a high-performance global analytics organization and developed new data products during a phase of rapid growth and expansion. Prior to that, he was a co-founder of Orchard, a pioneering data, analytics and transaction platform that accelerated the growth and institutionalization of online lending during a period of great stature and was acquired by Kabbage in 2018. Prior to Orchard, David held various leadership roles in analysis, product development and risk management at American Express, Citigroup and Oyster.com. He is a frequent writer and speaker on financial technology, credit, and the future of data-driven businesses and their impact on society.
Responsible for leading the development of advanced analytical solutions that enable financial services companies to make high quality decisions with reliable data.