Consumer Lending Discrimination in the FinTech Era

Abstract

Discrimination in lending can occur either in face-to-face decisions or in algorithmic scoring. We provide a workable interpretation of the courts’ legitimate-business-necessity defense of statistical discrimination. We then estimate the extent of racial/ethnic discrimination in the largest consumer-lending market using an identification afforded by the pricing of mortgage credit risk by Fannie Mae and Freddie Mac. We find that lenders charge Latinx/African-American borrowers 7.9 and 3.6 basis points more for purchase and refinance mortgages respectively, costing them $765M in aggregate per year in extra interest. FinTech algorithms also discriminate, but 40% less than face-to-face lenders. These results are consistent with both FinTech and non-FinTech lenders extracting monopoly rents in weaker competitive environments or profiling borrowers on low-shopping behavior. Such strategic pricing is not illegal per se, but under the law, it cannot result in discrimination. The lower levels of price discrimination by algorithms suggests that removing face-to-face interactions can reduce discrimination. Further silver linings emerge in the FinTech era: (1) Discrimination is declining; algorithmic lending may have increased competition or encouraged more shopping with the ease of platform applications. (2) We find that 0.74-1.3 million minority applications were rejected between 2009 and 2015 due to discrimination; however, FinTechs do not discriminate in loan approval.

Keywords: Discrimination, FinTech, Mortgages, Credit Scoring, Algorithmic Underwriting, Big Data Lending, Platform Loans, Disparate Impact, Legitimate Business Necessity

JEL Classification: G21, G28, G23, J14, K22, K23, R30