Determinants of Default in P2P Lending
Carlos Serrano-Cinca1, Begoña Gutiérrez-Nieto1, Luz López-Palacios1
1Department of Accounting and Finance, University of Zaragoza, Zaragoza, Spain.
Plos One
|October 2, 2015
Summary
Peer-to-peer (P2P) lending involves individual investors managing credit risk. Loan purpose, income, housing, credit history, and debt level significantly predict borrower default, with P2P site grades being key indicators.
Area of Science:
- Economics
- Finance
- Data Science
Background:
- Peer-to-peer (P2P) lending shifts credit risk from financial institutions to individual investors.
- Information asymmetry between borrowers and P2P lenders poses a significant challenge.
- P2P platforms provide borrower information and loan grades to mitigate this asymmetry.
Purpose of the Study:
- To identify key factors influencing loan default in the P2P lending market.
- To develop a predictive model for loan defaults in P2P lending.
- To assess the predictive power of P2P platform-assigned loan grades and borrower-specific data.
Main Methods:
- Analysis of a large dataset (N=24,449) of Lending Club loans from 2008-2014.
- Application of univariate means tests and survival analysis to identify default factors.
- Development of a logistic regression model for default prediction.
Main Results:
- Loan purpose, annual income, housing situation, credit history, and indebtedness were identified as significant predictors of default.
- The P2P lending site's assigned grade is the most influential factor in predicting default.
- Incorporating borrower debt level into the model significantly enhances default prediction accuracy.
Conclusions:
- P2P lending platforms play a crucial role in risk assessment through loan grading.
- Borrower financial characteristics, particularly debt levels, are vital for improving default prediction models.
- Understanding these factors is essential for mitigating risks in P2P lending investments.
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