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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.

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We extend our thanks to the staff of the City of Oakland, whose foresight in implementing the survey used in this paper and their countless hours of work on it are a testament to the desire to support the people and small businesses of Oakland. Particular thanks go to Marisa Raya. We also want to thank the SafeGraph and HomeBase companies who have generously shared their data to for research related to COVID-19's impact on U.S. businesses. Finally, we thank Annette Vissing-Jorgensen for feedback and the Berkeley Center for Law and Business for support. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

We extend our thanks to the staff of the City of Oakland, whose foresight in implementing the survey used in this paper and their countless hours of work on it are a testament to the desire to support the people and small businesses of Oakland. Particular thanks go to Marisa Raya. We also want to thank the SafeGraph and HomeBase companies who have generously shared their data to for research related to COVID-19's impact on U.S. businesses. Finally, we thank Annette Vissing-Jorgensen for feedback and the Berkeley Center for Law and Business for support. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.