Regulatory Arbitrage or Random Errors? Implications of Race Prediction Algorithms in Fair Lending Analysis

117 Pages Posted: 25 Apr 2023 Last revised: 13 Mar 2024

See all articles by Daniel Greenwald

Daniel Greenwald

New York University (NYU) - Leonard N. Stern School of Business

Sabrina T Howell

New York University (NYU) - Leonard N. Stern School of Business; National Bureau of Economic Research (NBER)

Cangyuan Li

New York University (NYU) - Leonard N. Stern School of Business

Emmanuel Yimfor

Columbia Business School

Multiple version iconThere are 2 versions of this paper

Date Written: January 30, 2024

Abstract

When race is not directly observed, regulators and analysts commonly predict it using algorithms based on last name and address. In small business lending—where regulators assess fair lending law compliance using the Bayesian Improved Surname Geocoding (BISG) algorithm—we document large prediction errors among Black Americans. The errors bias measured racial disparities in loan approval rates downward by 43%, with greater bias for traditional vs. fintech lenders. Regulation using self-identified race would increase lending to Black borrowers, but also shift lending toward affluent areas because errors correlate with socioeconomics. Overall, using race proxies in policymaking and research presents challenges.

Keywords: race prediction algorithms, racial disparities, small business lending

JEL Classification: G21, G23, G28, J15, C81

Suggested Citation

Greenwald, Daniel and Howell, Sabrina T and Li, Cangyuan and Yimfor, Emmanuel, Regulatory Arbitrage or Random Errors? Implications of Race Prediction Algorithms in Fair Lending Analysis (January 30, 2024). Columbia Business School Research Paper No. 4417513, Available at SSRN: https://ssrn.com/abstract=4417513 or http://dx.doi.org/10.2139/ssrn.4417513

Daniel Greenwald

New York University (NYU) - Leonard N. Stern School of Business ( email )

Sabrina T Howell (Contact Author)

New York University (NYU) - Leonard N. Stern School of Business ( email )

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National Bureau of Economic Research (NBER) ( email )

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Cangyuan Li

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

Emmanuel Yimfor

Columbia Business School ( email )

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New York, NY 10027
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