Research On Risk Assessment And Warning Of P2P Platform

Internet finance is a new financial model that combines traditional finance with Internet to realize financing,investment,payment and information intermediary services.P2 P online lending platform(hereinafter referred to as P2 P platform)is a kind of private micro-loan model that gathers small amount of money to lend to people who need money.It is an important part of Internet finance and inclusive finance.P2 P platforms have expanded financing channels for small and medium-sized enterprises.To a certain extent,it solves the problem of financing difficulty for small and medium-sized enterprises,and also provides people with a financial channel with better liquidity and higher returns.However,P2 P platform thunderstorms have occurred frequently in recent years,so risk assessment,control and prediction are important issues for government regulators,P2 P platforms themselves and investors to solve at present.This paper focuses on risk assessment and early warning of P2 P platform.First of all,this paper sorted out the risks faced by P2 P platforms and selected relevant indicators to establish a risk assessment index system.Compliance indicators under compliance risks are concluded by reading regulatory documents issued by regulatory departments and combining with the availability of indicator data.Including "whether there is a bank deposit and management","whether there is an ICP number","whether the registered capital is in compliance","whether there is a small amount of dispersion" and "whether there is a term mismatch",the compliance indicators are relatively perfect compared with those established by previous scholars.Secondly,250 platforms with relatively comprehensive data that survived at the end of May 2018 were selected from wangdaizhijia as the sample platform.By the end of May 2019,35 of the 250 platforms were thunderstorms and 215 platforms survived.The sample data range was from January 2018 to May 2019.Logistic regression model was used to analyze the factors affecting P2 P platform thunderstorms and the accuracy of model prediction.Again,choose 250 platform in June 2018 mutation progression model empirical prediction effect of the data,in June 2018-May 2019 to predict evaluated,platform will use mutation progression method to calculate the total risk value contrast table,the level of risk early warning signal will be platform for risk classification,to predict the survival state of the platform,and analyzes the prediction effect.Then,live 215 platform for the latest issue of the risk assessment and early warning,excluding the 19 undisclosed data platform,the remaining 196 platform USES data in May 2019 in mutation progression model to calculate the risk value of the corresponding risk assessment,then compare the risk early warning level table for early warning signal,and analyzed the results of risk assessment and early warning.Finally,the following conclusions are drawn: first,compared with the prediction accuracy of the Logistic regression model 95.2%,the overall prediction rate of the abrupt progression model is lower,but the accuracy of predicting the thunderstorm platform concerned by the supervision department is 88.6% higher than that of the Logistic regression model 74.3%.The risk of false rejection is higher and the risk of false acceptance is lower in the mutation series model,and the model is relatively "conservative".The false rejection only increases the supervision cost of the supervision department,but the false acceptance may lead to platform thunderstorms due to the lack of timely supervision.Obviously,the loss caused by thunderstorms is far greater than the supervision cost of the supervision department,so the mutation series model is more consistent with the supervision requirements.Second,Logistic regression results showed that six indicators,namely,guarantee mode,trading volume/waiting receipt,average borrowing term,maturity mismatch,average expected rate of return and net capital inflow,had a significant impact on whether P2 P platforms were thunderstorms.Through key monitoring of these six indicators,it was helpful for regulators to identify and supervise problem platforms.Third,compared with the Logistic regression model,the results of the abrupt progression model can more directly reflect the risk status of each platform and the ranking of the platform in the industry.The empirical results show that 58 out of 196 platforms are in early warning state.According to this result,regulators can focus on monitoring the platforms that are warned,so as to reduce the possibility of platform thunderstorms.Investors can also refer to this result to choose investment platform,so as to reduce investment risk.Fourth,see from the result of risk assessment and early warning,north zhejiang four areas,zhejiang P2 P platform overall risk condition,the best and worst P2 P platform overall risk condition of Shanghai,Shanghai is warning of a P2 P platform accounted for 54.5% of the total number of proportion,the biggest risk is higher,regulators should focus on the P2 P platform of Shanghai.In terms of compliance,P2 P platform has a high compliance rate in terms of bank deposit and management and small amount of decentralization,and is not ideal in terms of registered capital and term mismatch.The compliance rate of ICP number index is the lowest,only 31.63%.The compliance status of P2 P platforms in Beijing,Shanghai,guangzhou and zhejiang ranked in the order of zhejiang > Beijing > Shanghai > guangdong province.According to the conclusion of empirical research,the following countermeasures and Suggestions were put forward for government regulatory departments,P2 P platforms and investors: establish risk assessment and early warning mechanism,continue to promote compliance filing,and improve information disclosure management system;Reduce costs to achieve profitability,cooperate with the implementation of regulatory policies to achieve compliance operations;Establish correct investment concept and improve investment decision-making ability.At the same time,it also makes Suggestions for the supervision and development of similar new inclusive financial model in the future.