Screening Peers Softly: Inferring the Quality of Small Borrowers

• 成果类型: Article
• 署名作者: Iyer, Rajkamal; Khwaja, Asim Ijaz; Luttmer, Erzo F. P.; Shue, Kelly
• 署名单位: Massachusetts Institute of Technology (MIT); Harvard University; Dartmouth College; National Bureau of Economic Research; University of Chicago
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2015.2181
• 发表日期: 2016
• 页码: 1554-1577
• 关键词: peer-to-peer credit markets; market-based lending; crowd sourcing; screening; market inference; information and hierarchies; Soft information

摘要

This paper examines the performance of new online lending markets that rely on nonexpert individuals to screen their peers' creditworthiness. We find that these peer lenders predict an individual's likelihood of defaulting on a loan with 45% greater accuracy than the borrower's exact credit score (unobserved by the lenders, who only see a credit category). Moreover, peer lenders achieve 87% of the predictive power of an econometrician who observes all standard financial information about borrowers. Screening through soft or nonstandard information is relatively more important when evaluating lower-quality borrowers. Our results highlight how aggregating over the views of peers and leveraging nonstandard information can enhance lending efficiency.