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

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 compliance with fair lending laws 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 40%, with greater bias for traditional vs. fintech lenders. Since errors correlate with socioeconomic characteristics, basing regulation on self-identified race would increase lending to Black borrowers, but also shift lending toward affluent areas.

Our results highlight systematic problems with policies based on race proxies.

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