Research On Credit Risk Of P2P Lending

Nowadays,P2P lending is developing rapidly in China with advantages of low investment threshold,simple operation mode,high yield and so on,the number of P2P lending platform and the scale of the transaction grow quickly year after year.However,with the default of borrowers,the collapse of P2P platforms and other negative events happened frequently in recent years,the confidence of investors has been hit hard.It can be said that the credit risk has become the biggest obstacle to the healthy development of the P2P industry nowadays.If the default events of P2P lending are not effectively controlled,over time,the development of P2P lending will be affected negatively.However,due to that P2P lending is a new industry,the time of its birth and development is very short,the current analysis and evaluation research of its credit risk is still in the initial stage.At present,many domestic scholars use qualitative analysis method to study and evaluate the credit risk of borrowers,and lack the quantitative research method to research it.Therefore,its necessary to strengthen the analysis of the factors which influence the credit risk of P2P lending futher,enhance the quantized research of the credit risk of P2P lending futher and put forward more effective policy measures and suggestions of its credit risk control.It is of great practical significance to improve the credit risk evaluation mechanism of borrowers,ensure the quality of loans for P2P platforms and promote the sustained and healthy development of the whole P2P lending industry.This paper selects the domestic well-known P2P lending platform Renrendai as the research object,on the basis of a comprehensive analysis of its operation mode and its credit risk problem,by means of data mining technology,we will use factor analysis and Logistic regression to study the influence of borrowing information(such as marital status,age,education,work experience,credit rating,real estate the car,property status,average monthly income,the total amount of the loan,the annual interest rate etc.)on borrower's credit risk quantitatively,in order to construct the default probability model of the borrower and give the platform itself and the government supervision a lot of effective measures to control the credit risk of P2P lengding.