Algorithmic Lending, Competition, and Strategic Information Disclosure

55 Pages Posted: 16 Feb 2022 Last revised: 12 Nov 2024

See all articles by Qiaochu Wang

Qiaochu Wang

Carnegie Mellon University - David A. Tepper School of Business

Yan Huang

Carnegie Mellon University - David A. Tepper School of Business

Param Vir Singh

Carnegie Mellon University - David A. Tepper School of Business

Date Written: April 16, 2023

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

Suggested Citation

Wang, Qiaochu and Huang, Yan and Singh, Param Vir, Algorithmic Lending, Competition, and Strategic Information Disclosure (April 16, 2023). Available at SSRN: https://ssrn.com/abstract=4000045 or http://dx.doi.org/10.2139/ssrn.4000045

Qiaochu Wang

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Yan Huang

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Param Vir Singh (Contact Author)

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States
412-268-3585 (Phone)

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