Quantitative Assessment Of P2P Lending Credit Risk

Peer-to-peer lending(P2P)is an innovative financial model with Internet technology as the core.With convenient transaction and high coverage of users,it can effectively improve the transfer efficiency of social financial capital,having played an irreplaceable role compared with traditional banking.However,compared with traditional financial lending,P2 P puts financial platforms and investors at huge credit default risks from borrowers under asymmetric information.Compared with developed countries,China's P2 P market is still underdeveloped with poor management,and the problems caused by credit risk are very common among the platforms.Therefore,constructing a scientific and efficient system of credit risk management is the core issue that the P2 P industry needs to deal with for sustainable development.Compared with China,the credit system in the United States is more advanced,and the world's largest P2 P platform,Lending Club,started off in there.The success of Lending Club can be attributed to its excellent credit risk assessment system,with the big data analysis as the core of Lending Club's credit risk control.Aiming at solving the problem that it is hard for current P2 P platforms to carry out the borrower credit risk assessment,this paper draws on the big data credit risk control system of the global P2 P leading platform——Lending Club,to explore the application of big data analysis technology in P2 P credit assessment,hence improving the credit risk management level of P2 P enterprises.This paper first reviews recent researches on Internet financial risk management,identification of credit risk factors,and credit risk assessment models at home and abroad.Secondly,it elaborates the credit risk management of current domestic P2 P platforms and summarizes the existing problems,and then analyzing the operating model and the risk control system of “Lending Club”.It focuses on Lending Club's big data credit risk control mechanism.The empirical part of this article uses the real lending data from “Lending Club” to conduct credit scoring modeling.The data contains 1,373,252 pieces of samples,each of which has 144 fields.After data analysis and feature preprocessing,16 fields are finally selected to participate in modeling.Considering the significance of model interpretability,this paper mainly studies the modeling based on logistic regression algorithm and evaluates the model by using indicators of KS and AUC.The final modeling results show that the model has a KS value of 0.301 and an AUC value of 0.707.Through credit scoring,the credit risk of the borrower is quantified,which proves a good effect of big data mining technology on P2 P credit evaluation in practice.Finally,based on the results of the empirical analysis,this thesis summarizes what is worthful to learn from Lending Club for domestic P2 P platforms,and puts forward corresponding suggestions on enhancing the credit risk management capabilities of China's P2 P industry in the aspects of the P2 P lending platform's own operation and national policies.