The COVID crisis has starkly revealed the thin line between middle-class status and destitution in the United States. As a Greater Depression looms, vital assistance from the federal government may soon expire. At that point, the unemployed may need to seek loans for necessities, ranging from rent to food to health care. Advocates for a “public option” in finance have pressed ideas like postal banking or “quantitative easing for the people,” to enable direct government provision of lending for those the market is not serving. They have met a wall of opposition, particularly from libertarian advocates of cyber finance. The tech solutionist alternative is simple: instead of direct government lending, let new financial technology (fintech) companies accumulate more data, and then they can precisely calibrate optimal loan amounts and interest rates. Algorithmic lending, cryptocurrency, and smart contracts all have a place in this vision.
Christopher Odinet’s important article Consumer Bitcredit and Fintech Lending challenges this conventional wisdom, demonstrating that some fintech business models rely on deeply predatory and unfair treatment of borrowers. Through both qualitative and quantitative analysis of over 500 complaints from a Consumer Financial Protection Board (CFPB) dataset, Odinet paints a grim picture of fintech malfeasance. Cyberlenders may be a route for financial inclusion for many—but they also pose risks that are poorly understood, and nearly impossible to protect against.
Odinet painstakingly documents and classifies actual consumer complaints, adding an invaluable empirical foundation to widespread worries about the potential for predatory financial inclusion by new entrants in the consumer lending space. I wish I had Odinet’s article when I testified before the Senate Banking Committee on fintech in 2017. Key senators and Trump Administration officials clearly wanted to accelerate deregulation; Odinet shows the importance of an enduring role for both federal and state regulators in this space.
Here are just a few of the narratives Odinet unearths in consumer complaints:
From a borrower trying to auto-pay a loan: “They are outrageous with regard to how many problems they create to prevent you from paying your monthly installment. Clearly, they are trying to get consumers to default, so they can jab you with excessive late (and other) fees.”
From a borrower who paid off her loan in full, only to continue being debited: They “debited my account for bill and grocery money that i [sic] needed to take care of my family.”
From a borrower surprised by a large “origination fee: “The loan documentation was not available until the loan was funded and there is nothing in the documentation that indicates the origination fee that would be charged.”
From a borrower behind on payments: “This company calls every hour on the hour.”
From a borrower stuck with a high interest rate: “I was told, that after 1 yr. I was going to be able to lower my interested [sic] rate on [my] debt consolidation loan. But, it turns out, that I have to reapply & pay another lending club processing fee. The rate is ridiculously high compare [sic] to current rates. I only took this loan in desperation.”
Other entities appear to be harvesting sensitive financial information from loan applicants, then disappearing without actually funding loans.
Odinet complements these narratives with pie charts classifying complaints. He finds that “the largest number of complaints (over half) relate to how the loan was managed. The next highest category deals with taking out a loan.” His empirical analysis deftly visualizes government data in an accessible manner. It also has immense policy relevance. Emboldened by fintech utopianism, many regulators have loosened the reins for new firms. But this is a misguided approach, since the use of AI in fintech has just as many problems as traditional underwriting—if not more.
Odinet’s work also helped me suss out a paradox in fintech valuation. Investors have justified pouring money into this sector based on the prospect of ever-improving AI finding ever more profit opportunities than older statistical methods. However, I’ve also been to presentations by experts on finance algorithms convincingly demonstrating that past repayment history is powerfully predictive of future conduct, and that additional “fringe” or “nontraditional” data adds little to the predictive calculus. So how are fintechs supposed to make above market returns if their “secret sauce” in reality adds so little to their predictive capacities? As expertly interpreted by Odinet, the CFPB complaints database suggests a ready route to profitability: hiding good old fashioned cheating, sharp business practices, and dark patterns behind a shiny veneer of futuristic AI. Here, Odinet follows in the footsteps of many scholars who have exposed deep problems in an allegedly new digital economy (including platform capitalism and initial coin offerings). All too often, a narrative of technological advance masks old, disfavored, and illegal practices.
Of course, there will always be rival narratives about the value and dangers of algorithmic lending and fintech platforms. They do extend credit to some individuals who would find no conventional alternatives. Odinet offers important data here that will be of use to both advocates and critics of fintech. He complements his expert and compelling empirical findings with accessible explanations of why they matter. He grounds recommendations for regulatory responses on the empirical findings in this article, focusing on the need for relevant agencies to better understand fintechs’ business models, to detect and deter discrimination, and to ensure more effective disclosures. This is important work that will help governments around the world develop data-informed approaches to the regulation of fintech.