Research On The Credit Rating Structure Of P2P Platforms In China

In recent years,the rapid development of Internet finance marks the peak of P2P lending industry,but at the same time,the thunder of many P2P platforms makes the financial market suffer great losses and turbulence,so it is urgent to establish an appropriate credit rating model.According to the particularity of P2P lending industry in China,this paper constructs a credit rating model suitable for P2P lending platform in China,which has strong practical significance.This paper mainly introduces the following contents:Firstly,the necessity of establishing P2P platform credit rating model is analyzed.After understanding the practical significance of this topic,this paper analyzes the shortcomings of existing credit rating models at home and abroad.Secondly,the credit rating model of P2P lending platform is constructed.According to our country the uniqueness of P2P platform,selecting suitable for our country the P2P lending platform of non-financial indicators and the financial indicators,the non-financial indicators respectively from four angles:the enterprise's basic quality,enterprise's management ability,the enterprise credit behavior and enterprise development potential and expert evaluation method is used to calculate the non-financial indicators weight.SPSS was used as principal component analysis for the 13 initially selected financial indicators,and the indicators with big contribution rate were screened out to finally determine 12 financial indicators.Then,partial correlation analysis was used to gradually determine the weight of each indicator.Finally,a credit rating model applicable to China's P2P platform was established.Finally,the case study of renrendai is carried out to analyze the four aspects of renrendai's non-financial indicators.Then,the financial indicators are analyzed and put into the credit rating model to test the feasibility of the above credit rating model.In addition,30 P2P platforms were selected to score them with the above model,and the obtained scores were analyzed for their degree of fitting with the social sciences score,and the final degree of fitting was relatively high.Moreover,the scores of financial data of 30 P2P platforms are analyzed by fitting degree with those of case to test the influence of non-financial data,and finally the necessity of non-financial data is concluded.