Escaping the Subprime Trap in Algorithmic Lending

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 ($\VaR$) and Expected Shortfall (ES), in inducing inequality in loan approval decisions, even among applicants who are equally creditworthy. Empirical research finds that disparities in the interest rates charged to minority groups can remain large even when loan applicants from different groups are equally creditworthy. We contribute an original 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, thus 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 lenders can borrow only from high-cost lenders, even when they are as creditworthy as majority applicants. Finally, we show that a finite subsidy can help minority groups escape the trap: subsidies cover enough of the mainstream bank's downside risk 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, it approves the applications of minority groups, and competition drives down the interest rates of subprime loans.