启发式
贷款
启发式
精算学
违约
计算机科学
偏爱
点对点
业务
经济
财务
人工智能
微观经济学
操作系统
分布式计算
作者
Maggie Hu,Xiaoyang Li,Xitong Li,Xiaoquan Zhang
标识
DOI:10.1287/isre.2023.1202
摘要
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.
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