Modeling Default for Peer-to-Peer Loans
43 Pages Posted: 23 Nov 2014
Date Written: November 21, 2014
I use a discrete-time hazard model to analyze default for peer-to-peer (P2P) loans. My data set is large, publicly available, and includes both extensive credit information and soft information. This combination of features, which is unique to P2P data sets, allows for a more thorough analysis of consumer credit than is possible with data from traditional intermediaries. FICO score, borrower-initiated credit inquiries, income, and loan purpose are the most significant variables for explaining default. Two variables extracted from loan descriptions filled out by borrowers are also significant. Several variables typically thought to be significant predictors of default, including income verification and past bankruptcies, are not significant. My model substantially outperforms Lending Club subgrades for forecasting default.
Keywords: consumer credit, peer-to-peer lending
JEL Classification: G21
Suggested Citation: Suggested Citation