Algorithmic Lending, Competition, and Strategic Information Disclosure

Abstract

Machine learning algorithms are increasingly used to evaluate borrower creditworthiness in financial lending, yet many lenders do not provide pre-approval tools that could significantly benefit consumers. These tools are essential for reducing consumer uncertainty and improving financial decision-making. This paper examines why symmetric lenders, with equal non-price features and algorithmic accuracy, might asymmetrically reveal pre-approval outcomes. Using a multi-stage game theory model, we analyze the strategic decisions of duopoly lenders in offering pre-approval tools for unsecured financial products. Our findings reveal that high algorithm accuracy can sustain an asymmetric revelation equilibrium, with one lender disclosing pre-approval outcomes while the other does not. Conversely, low algorithm accuracy prompts both lenders to reveal pre-approval outcomes. These findings diverge from traditional literature, which typically associates asymmetric revelation with differentiated products. Additionally, our results show that mandatory revelation policies could reduce lenders' incentives to improve algorithmic accuracy, potentially harming social welfare. These insights inform managerial strategies on the use of algorithmic transparency in lending and underscore the need for careful consideration of regulatory policies to balance market efficiency and consumer protection.

Keywords: consumer finance, credit approval, pre-approval odds, competition, fintech, machine learning, financial intermediary, game theory.

JEL Classification: L1, M00