Analytics and Automation Are Opening the Credit Markets to New Consumers
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Fintech’s Access Advantage: Alt Data and Analytics Are Opening the Credit Markets to New Consumers
A new economy needs a new approach to underwriting; fintechs are stepping in to fill pronounced gaps and rapidly growing marketshare along the way.
The conventional wisdom amid the recent backlash against all things technology is that AI and machine learning are at worst inherently biased or at best only serve one segment of the population. Last year, for instance, the Committee on Financial Services Task Force held a hearing on “equitable AI” in which Congressman Barry Loudermilk proposed, “There needs to be a benchmark to compare algorithm results and evaluate the fairness of an algorithm’s decisions.” The good news is that there is already a way to benchmark the fairness of fintech platforms; the bad news is that it exposes the biases of the status quo (ie, the unexplainable “black box” that is the human mind).
To wit, research out of New York University recently documented that among PPP lenders, fintech platforms as a whole were far more effective than traditional banking institutions in allocating loans across a more diverse population of borrowers. For instance, the data showed that fintech lenders had a considerably larger share of their total number of loans that went to black business owners than any other lender category, from small- and medium-sized banks to bulge bracket institutions. While the study focused on business loans, it underscores the potential for fintech platforms to have the same impact on personal finance.
This is not to say that there isn’t room for improvement or that neural network technology has totally solved the issue of bias and access in consumer banking. There is, indeed, work to be done. But to suggest that fintech platforms haven’t already created a better alternative for either underserved communities or less-than-clear-cut credit profiles is to overlook the myriad ways that technology is already facilitating access for consumers.
The “efficiency” story is universal to most digital solutions, although the historic bottlenecks that have constrained consumer lending are probably more pronounced than anywhere else in finance. Automation, for instance, is allowing lenders to process mortgage loans in mere days versus weeks or months. A more intuitive user experience is also eliminating excessive switching costs that have traditionally tilted the playing field in favor of legacy institutions. If consumers get rejected or don’t like the terms offered by their current banking relationships, alternatives are just a few clicks away. Automation and speed, in this regard, are already translating into more choice for consumers, more power to dictate improved terms, and better, more personalized outcomes.
All that said, it’s the growth and adoption of alternative data and analytics that has the potential for a far bigger impact as it relates to access by empowering decision makers with infinitely deeper granularity and more revealing insights about the credit-worthiness or risks of prospective borrowers.
Addressing FICO’s Blind Spots
Few would argue that technology doesn’t yield material efficiencies for either fintech lenders underwriting consumer loans or individuals applying for them. Even Jamie Dimon, in JPMorgan Chase’s annual shareholder letter, warned that significant marketshare is at risk given the ability of fintechs to seamlessly “integrate with other platforms'' and “use data smartly.”
However, the upstarts aren’t merely targeting existing customers; they’re also bringing previously unbanked consumers into the fold with more dynamic underwriting models and mass-customized analytics that enable bespoke credit solutions. When these platforms and applications gain critical mass, it will represent a sea-change transformation over the current state in which most prospective borrowers are evaluated based on a cut-and-dried credit profile, marked by glaring gaps of information.
When the credit bureaus were established in the 1970s, they were instrumental in helping to democratize finance and provided structure where there previously wasn’t any. 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 likelihood of default. FICO’s scoring is based on four primary inputs – a borrower’s outstanding credit, their repayment history, delinquencies, and credit inquiries. Again, 50 years ago, FICO’s credit scores were groundbreaking. They offered a trackable and somewhat objective measure to assess risk. And, make no mistake, an individual’s past track record of paying debt is indeed a good indicator of their ability to pay debt in the future.
Fintech lenders today, though, are benefitting from a more complete picture -- through data -- that goes above and beyond credit scores and traditional reports. It’s not just about underwriting with more precision and conviction; alternative data and new analytics are allowing fintech platforms to monitor their credit portfolios more diligently, while also expanding the scope of issuance without altering the risk profile of the credits they take on.
The benefits to borrowers are even more pronounced. Access to income and deposit data, for instance, opens up financing to a whole range of individuals who represent low-risk credits, but struggle to fit into FICO’s narrow archetype of credit worthiness. Information about how exactly individuals are spending their money, too, is equally valuable, and arguably far more relevant to measure a borrower’s ability to pay down a loan. The credit bureaus, today, can only access outflows to pay existing debt, which represents a very thin slice of an individual’s financial identity. This newfound granularity also allows fintech to offer hyper-personalized solutions in which the risk premiums match the specific borrower and their unique, real-time financial circumstances.
The Big Gig Opportunity
Consider how the economy has evolved over the past 50 years. Lenders simply need a new approach to reframe how they make credit decisions -- as risk models change, as occupations evolve, and as employee and employer relationships become more fluid. The growth of the gig economy offers a prime example.
More than a third of U.S. workers are independently employed according to government estimates, and over half of the Gen Z workforce is active in the gig economy. Depending on the specific field, gig workers often out-earn more traditional employees in comparable roles, particularly in transportation, professional services, and healthcare professions, according to data from ADP comparing 1099-MISC employees and traditional W-2 filers. Still, underwriting gig economy workers can be more challenging for lenders, given the “seasonality” of the specific business, employer verification hurdles and other nuances.
Technology is providing a tool for lenders to fill in the informational gaps and new entrants are indeed exploiting these capabilities to grab marketshare.
Jack LaMar, head of GTM Strategy, New Products, at Plaid, remarked at a recent webinar that when Uber and Lyft gained critical mass, the neobanks were not far behind. He observed that a growing population have oriented their entire value proposition around one goal -- “to serve gig economy employees. That’s it.”
Credit has always been a proxy for confidence – in the economy, in ourselves, and in our collective future. Individuals take on loans in pursuit of a vision. Lenders provide credit because they have faith – backed up by data – that borrowers will be able to repay their debt.
Credit, as such, is the fuel that drives the economy. But when people can’t access financing, whether due to subconscious biases, irregular income streams, or even a lack of a credit history, it creates a handicap that leaves entire segments of the population behind. It may sound like hyperbole, but many fintechs that are looking to disrupt the status quo are doing so because they recognize there’s a huge opportunity just by creating a level playing field. Access, they’re discovering, doesn’t entail a leap of faith; it just requires a different perspective, one that’s now available through alternative data and analytics.
About the Author
David Snitkof is a technology entrepreneur and data/analytics leader with a successful track record of developing analytical systems, teams, and businesses from the ground up. He was most recently Head of Analytics and Data 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 co-founder of Orchard, a pioneering data, analytics, and transaction platform that accelerated the growth and institutionalization of Online Lending during a time of massive scale and was acquired by Kabbage in 2018. Prior to Orchard, David held various analytical, product development, and risk management leadership roles at American Express, Citigroup, and Oyster.com. He is a frequent writer and speaker on financial technology, credit, and the future of data-driven business and its impact on society.
Responsible for leading the development of advanced analytical solutions that enable financial services companies to make high quality decisions with trusted data.