PhD Public Lecture (Math) - Jingshi Luo

"Credit Risk Measurement and Application based on BP Neural Networks"

The emergence of P2P(Peer-to-peer) lending has opened up a popular way for micro-finance, and the financial lending industry in many countries is growing rapidly. While it facilitates lending to individuals and small and medium-sized enterprises, improving the risk identification capability of the P2P platform is vitally necessary for the sustainable development of the platform. Especially the potential credit risk caused by information asymmetry, this may be fatal to this industry. In order to alleviate the adverse effects of this problem, this paper takes Lending Club’s real loan data as the empirical research object. The random forest is used to screen the importance of features, and backpropagation neural network approach is used to establish a credit risk classification model. Before loaning, the loan applicants can be divided into default and non-default. The results show that the credit risk measurement model is effective in predicting whether the lender will default.