Research On Credit Risk Assessment Of P2P Lending In China

As an important part of the Internet financial, P2 P loans obtained the swift and violent development in recent years. Rapid development in the industry at the same time, the industry faces is more and more big, the risk of the society the attention to its risk is also increasing. Based on the study of domestic and foreign credit evaluation theory and method, on the basis of deep thinking from the aspects of the P2 P network credit industry possible risk, and attempts to start with the lender, again from net credit platform, and then to the P2 P network lending industry, the overall comprehensive considering P2 P network of credit risk.First, since the network industry in a typical loan risk that the borrower default risk, so the first part is the study of the possible risk of default, borrowers to research as a starting point, comparative analysis of the borrower credit risk assessment both at home and abroad, and combined with the actual user’s net lending company in China, a new P2 P network of credit risk assessment index system, and by using the analytic hierarchy process(AHP), the importance of the project to different criteria to classify, make the total score is more scientific and reasonable.To end up with the borrower credit risk assessment method, will use the online test in the net credit blacklist, verify the practical accuracy of the method.Second, in the face of domestic P2 P network platform loan "run" andthe frequent failures, great negative influence on P2 P industry, this paper,by using network home loan to collect 320 net platform loan volume, the number of investment, the average interest rate, average index data, such as the period by looking for personality characteristics of different platforms, find outliers, risk early warning. Home loan is China’s first and largest net credit third party sites, good reputation and the authority of the data, the data of this paper is derived from the network home website. In this thesis choose to adopt the method of clustering analysis of data from the group of the main reason is: regular data output in the form of cluster cluster, depicting the main characteristic of the data to some extent, easier to interpret and understand the causes of the outliers, which can quickly find different from general platform of risky, and can be more intuitive to see it as the cause of the abnormal platform.Again, in order to let the parties focus on net credit risk more intuitive understanding of the network credit risk occurs possible maximum loss of value, the third part will use the combination of extreme value theory and VaR, the loss of data collected by P2 P industry POT model, parameter estimation method, choose fitting better method,calculate the VaR value network industry loan losses.