Numerological Heuristics and Credit Risk in Peer-to-Peer Lending

Author:

Hu Maggie Rong1ORCID,Li Xiaoyang2ORCID,Shi Yang3ORCID,Zhang Xiaoquan (Michael)45ORCID

Affiliation:

1. The Chinese University of Hong Kong Business School, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;

2. School of Accounting and Finance, PolyU Business School, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;

3. Department of Finance, Faculty of Business and Economics, The University of Melbourne, Melbourne, 3010 Victoria, Australia;

4. Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Shenzhen 518055, China;

5. The Chinese University of Hong Kong Business School, The Chinese University of Hong Kong, Hong Kong

Abstract

People often use heuristics as mental shortcuts when making financial decisions. Although the literature typically considers heuristics as behavior biases, we explore how different types of heuristics differ from one another. Through peer-to-peer lending data, we observe that borrowers who use limited attention when applying for loans tend to choose round loan amounts, simplifying the decision-making process but compromising accuracy. This round-number heuristic decreases their funding success rate and increases the probability of default. On the other hand, some borrowers select loan amounts in “lucky numbers” that superstitious lenders favor. This lucky-number heuristic caters to the lenders’ preference, thus increasing the borrowers’ funding success rates and reducing the likelihood of default. Our paper demonstrates that borrowers select heuristics based on their motives, leading to varying consequences. We also show that heuristics are not all the same, and people’s choice of heuristics provides insight into their characteristics and can predict decision outcomes. For instance, factoring in heuristic usage information improves default prediction accuracy in our setting. Our findings can be beneficial to practitioners in refining the underwriting and screening of borrowers and loans.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Library and Information Sciences,Information Systems and Management,Computer Networks and Communications,Information Systems,Management Information Systems

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Beyond Risk: A Measure of Distribution Uncertainty;Information Systems Research;2024-08-21

2. Knowledge graph driven credit risk assessment for micro, small and medium-sized enterprises;International Journal of Production Research;2023-09-12

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