Tens of millions of Americans lack access to traditional forms of credit and must rely on payday and pawn loans instead. “Algorithmic lending 2.0” promises to enable fintech companies to lend to those excluded from traditional forms of credit. Version 2.0 algorithmic lenders claim to use Big Data and machine learning to increase credit access by making better predictions about prospective borrowers’ creditworthiness and decreasing the cost of credit. Supporters also claim that algorithmic lending 2.0 removes human bias from the financial services sector. Detractors have cast doubt on both claims, arguing that there is scant evidence that algorithmic lending 2.0 expands credit access in non-predatory ways or that substituting algorithms for loan officers reduces discrimination. This Article evaluates the existing regulatory framework to determine if regulation can support the promise of algorithmic lending 2.0 without imperiling the vulnerable.
Matthew A. Bruckner,
The Promise and Perils of Algorithmic Lenders’ Use of Big Data,
Chi.-Kent L. Rev.
Available at: https://scholarship.kentlaw.iit.edu/cklawreview/vol93/iss1/1