Abstract
This paper examines the determinants of platform default risk using machine learning methods, including comprehensive models, and thus compares these models’ predictive abilities. To test platform’s default risk, this paper constructs three types of variables, which reflect a platform’s operating characteristics, customer feedback, and compliance capability. We find that the abnormal return tends to trigger default risk significantly. However, the default risk can be minimized if a platform has positive recommendations from customers and more transparent information disclosure or is affiliated as the member of the National Internet Finance Association of China. Empirical results indicate that the CART model outperforms the Random Forests model and Logit regression in predicting platform default risk. Our study sheds light on default risk prediction and thus can improve the government regulation ability.
Highlights
Default risk has long been a significant risk factor to test borrowers’ behaviour in Peer-to-Peer (P2P) lending
Lending platforms with high expected return and short payback period are more likely to have low default risk by using decision tree. e study in [6] further examines the relationship between soft information and P2P lending default risk in two European P2P lending platforms. eir experiments indicate that soft information such as the length of text, spelling mistakes, and the sentiment analysis of keywords generated from description text has a limited impact on the probability of default
Previous studies examine the platform default risk by using Probit regression and tree-based classifiers, respectively. Extending this stream of research, our study develops a comprehensive model including Logit, Classification and Regression Tree (CART), and Random Forests algorithms to deal with credit scoring problems and test platform’s default risk. erefore, the model is optimized to obtain unbiased estimation and higher precision
Summary
Default risk has long been a significant risk factor to test borrowers’ behaviour in Peer-to-Peer (P2P) lending. The study in [4] proposes that borrowers’ default risk of the Chinese P2P lending platform Renrendai is significantly influenced by borrower’s credit score and credit rate distribution. Previous studies examine the platform default risk by using Probit regression and tree-based classifiers, respectively Extending this stream of research, our study develops a comprehensive model including Logit, CART, and Random Forests algorithms to deal with credit scoring problems and test platform’s default risk. In China, platform default risk in P2P lending market is even more serious because of the lack of credit information system. E first contribution is to construct assessment determinants of default risk and figure out what factors effectively influence the operating status of P2P lending platforms. E information about online P2P lending platform’s risk is divided into three categories: operating characteristics, customer feedback, and regulatory compliance capability.
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