2026-07-20T14:23:30Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:25977 2026-06-23T13:06:51Z ddc:004 open_access

Escaping the Subprime Trap in Algorithmic Lending Bouyamourn, Adam Tolbert, Alexander Williams Algorithmic fairness algorithmic lending Risk management Value-at-Risk Algorithmic Philosophy Disparities in lending to minority applicants persist even as algorithmic lending finds widespread adoption. We study the role of risk-management constraints, specifically Value-at-Risk and Expected Shortfall, in inducing inequality in loan approval decisions, even among applicants who are equally creditworthy. We contribute an analysis of 431,551 loan applications recorded under the Home Mortgage Disclosure Act, illustrating that disparities in data quality are associated with higher rates of loan denial and higher interest rate spreads for Black borrowers. We develop a formal model in which a mainstream bank (low-interest) is more sensitive to variance risk than a subprime bank (high-interest). If the mainstream bank has an inflated prior belief about the variance of the minority group, it may deny that group credit indefinitely, never learning the true risk of lending to that group, while the subprime lender serves this population at higher rates. We call this "The Subprime Trap": an equilibrium in which minority borrowers can borrow only from high-cost lenders, even when they are as creditworthy as majority applicants. We show that a finite subsidy can help minority groups escape the trap by covering enough of the mainstream bank’s downside so that it can afford to lend to, and thereby learn the true risk of lending to, the minority group. Once the mainstream bank has observed sufficiently many loans, its beliefs converge to the true underlying risk, and competition drives down the interest rates of subprime loans. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Adam Bouyamourn and Alexander Williams Tolbert 2026 Is Part Of LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.FORC.2026.6 urn:nbn:de:0030-drops-259777 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.6 eng https://creativecommons.org/licenses/by/4.0/legalcode