Algorithmic Lending, Competition, and Strategic Information Disclosure

Machine learning (ML) algorithms used by financial lenders in their screening processes are hidden from the consumers who are affected by their decisions leading many consumers to make sub-optimal decisions when seeking credit. Despite increasing calls for greater transparency, only a few lenders provide personalized approval odds to consumers (e.g. via financial intermediaries like Credit Karma or pre-approval tools). We investigate how competition among algorithmic lenders affects their decisions to provide approval odds to consumers. We show that competitive pressures between lenders can undermine the disclosure incentives. Lenders use asymmetric disclosure of approval odds strategically to soften the competition when their algorithms are fairly accurate. The asymmetric disclosure of approval odds endogenously creates product differentiation and allows lenders to focus on different segments of consumers softening the competition on the interest rates. We find that consumer surplus is highest when both lenders provide approval odds and lowest when neither provides approval odds. However, our analysis also shows that any policy that mandates all lenders to provide personalized approval odds to consumers may not necessarily improve consumer surplus