Consumer-lending discrimination in the FinTech Era
By Robert P. Bartlett, Adair Morse, Richard Stanton, Nancy Wallace. Journal of Financial Economics, 2019
587 citations recorded by the source corpus.
Abstract U.S. fair-lending law prohibits lenders from making credit determinations that disparately affect minority borrowers if those determinations are based on characteristics unrelated to creditworthiness. Using an identification under this rule, we show risk-equivalent Latinx/Black borrowers pay significantly higher interest rates on GSE-securitized and FHA-insured loans, particularly in high-minority-share neighborhoods. We estimate these rate differences cost minority borrowers over $450 million yearly. FinTech lenders’ rate disparities were similar to those of non-Fintech lenders for GSE mortgages, but lower for FHA mortgages issued in 2009–2015 and for FHA refi mortgages issued in 2018–2019.
Subfields: Economics and Econometrics, Management Information Systems
View Paper: https://www.semanticscholar.org/paper/97a98065c1fec5c36104f586c93ca756caf5caaf
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Citations in the corpus
- Predictably Unequal? The Effects of Machine Learning on Credit Markets (2020, unknown): “Our focus in this paper is on the distributional impacts of changes in technology rather than on explicit taste-based discrimination (Becker, 1971) or “redlining ” which seeks to use geographical information to indirectly differentiate on the basis of excluded characteristics, and which is also expl”
- Fintech Borrowers: Lax Screening or Cream-Skimming? (2020, background): “On the one hand, this alternative data can increase access to credit for individuals with a short credit history or that are priced out of the market; on the other, some of the alternative variables could involuntarily discriminate against some group of borrowers (Bartlett et al., 2017).”
- Participatory provenance as representational auditing for AI-mediated public consultation (2026, unknown): “…has developed sophisticated frameworks for auditing classification errors in high-stakes decision systems such as hiring [Raghavan et al., 2020], lending [Bartlett et al., 2022], criminal justice [Chouldechova, 2017, Corbett-Davies et al., 2017] and facial recognition [Buo-lamwini and Gebru, 2018].”