Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6854079 | Electronic Commerce Research and Applications | 2018 | 22 Pages |
Abstract
Traditional behavioral scoring models applying classification methods that yield a static probability of default may ignore the borrowers' dynamic characteristics because borrower repayment behavior evolves dynamically. In this study, we propose a novel behavioral scoring model based on a mixture survival analysis framework to predict the dynamic probability of default over time in peer-to-peer (P2P) lending. A random forest is utilized to identify whether a borrower will default, and a random survival forest is introduced to model the time to default. The results of an empirical analysis on a Chinese P2P loan dataset show that the proposed ensemble mixture random forest (EMRF) has a better performance in terms of predicting the monthly dynamic probability of default, while compared with standard mixture cure model, Cox proportional hazards model and logistic regression. It is also concluded that the proposed EMRF model provides a meaningful output for timely post-loan risk management.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Zhao Wang, Cuiqing Jiang, Yong Ding, Xiaozhong Lyu, Yao Liu,