Algorithmic Lending, Competition, and Strategic Provision of Preapproval Tools

Machine learning algorithms are increasingly used to evaluate borrower creditworthiness in financial lending, yet many lenders do not provide preapproval 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 nonprice features and algorithmic accuracy, might asymmetrically reveal preapproval outcomes. Using a multistage game theory model, we analyze the strategic decisions of duopoly lenders in offering preapproval tools for unsecured financial products. Our findings suggest that high algorithm accuracy can sustain an asymmetric revelation equilibrium, with one lender revealing preapproval outcomes through preapproval tools whereas the other does not, even when there is no explicit cost of providing such preapproval tools. Conversely, low algorithm accuracy prompts both lenders to reveal preapproval outcomes. These findings diverge from traditional literature, which typically associates asymmetric revelation with differentiated products or revealing cost. 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.

History: Anthony Dukes served as the senior editor.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/mksc.2023.0164.