Consumer-Lending Discrimination in the FinTech Era∗
نویسندگان
چکیده
Loan officers may discriminate based on facial features. Algorithmic scoring may imply disparate impact due to the inappropriate use of big data by FinTech lenders. To investigate these discrimination channels, we estimate a treatment-based OaxacaBlinder decomposition based on the unique mortgage-default-risk setting of the GSEs. We find that lenders reject African-American and Hispanic applicants 5% more often, leaving money on the table. Discrimination is especially pronounced among low-creditscore applicants, but less pronounced for FinTech lenders. Lenders charge ethnicminority borrowers 0.08% higher interest for purchase mortgages and 0.03% higher for refinance mortgages. Our results point to lenders extracting monopoly rents in weaker competitive environments such as financial deserts. In aggregate, ethnic-minority borrowers pay almost $0.5B per year in extra interest. Finally, we document that the GSEs play a crucial role in minimizing algorithmic discrimination.
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