Research On Borrowers' Credit Risk Of Online Peer-to-peer (P2P)lending In China

Peer-to-Peer(P2P)network industry as one of the most active forms of Internet financial loan.When first foreign P2 P network credit platform ZOPA born then the introduction of the domestic financial sector shows the development trend of prosperity.Micro and medium enterprises and the low and middle income earners is difficult to obtain financing in China's traditional lending market,the rise of P2 P network industry loan just to make up for the traditional lending market.However,P2 P net lending industry in its rapid development also emerged a series of problems,one of the major challenges facing the P2 P network industry loan is the borrower's credit risk problem.These problems not only cause losses to the lender,but also for P2 P network industry loan credit caused great negative impact which seriously problem affected the further healthy development of credit industry P2 P network.Therefore,the focus of this paper to analyze the reasons for the borrower's borrowing risk,what factors affect the borrower's credit risk and how to prevent the borrower's credit risk.At present,domestic and foreign scholars which research P2 P network platform loan borrower credit risk rarely uses the combination of qualitative and quantitative methods.This paper studies uses the quantitative method of combining the qualitative study of such problems.First of all,combining with the P2 P network industry loan and representative of all credit platform were analyzed,and explain the root causes of the borrower credit risk is the asymmetric information.Main microscopic causes from five aspects,the lack of a mechanism of sharing the borrower credit,borrowers do not pay enough attention to information management,the credit rating system is not unified,lack of loan borrowers and lenders themselves,respectively;The main macro causes from four aspects,the imperfect credit system,imperfect laws and regulations,and lack of P2 P network credit self-discipline organization and independent publiccredit rating agency.Secondly,used Python crawler software grab everyone needed to borrow platform on the relevant information of the borrowers and through the Logistic regression model to empirical.The mainly two conclusions: first is choose the eight variables in the interpretation of the borrower's income,the included seven explain variables has significant effect on the borrower's credit risk;The second is the borrower credit risk is the most significant factors affecting is the borrower's loan amount and loan interest rates that negative influence factor is the most significant in the borrower's credit rating and degree.At last,through the micro and macro analysis level of P2 P network of loan borrower credit risk prevention put forward corresponding Suggestions.We wish to contributing to the future P2 P net lending industry efficient,sustained and healthy developing.