Research On Risk Evaluation Of Internet Credit Platform Based On BP Neural Network

In recent years,with the rapid development of Internet technology,Internet finance has gradually entered people's field of vision as a new financial situation and has been welcomed by people.Based on the broad demand market,the P2P online loan platform began to expand in a barbaric manner.However,the opposite of the birth of one online loan platform is the explosion of more online lending platforms.Observing the reasons for the thunder of the online loan platform,there are some companies that use the P2P heat to carry out illegal fund-raising and fraudulent loans,and some companies have their own risk control,which fails to control the quality and liquidity of the funds.A break in the capital chain has led to bankruptcy.The development of P2P has indeed brought us a lot of convenience,but its hidden huge risks must not be ignored.The phenomenon of thunder is so severe.How to identify and control the risk of online lending platform is imminent.Therefore,the risk evaluation of the online lending platform,its probability and model,has a practical significance for the healthy development of the P2P industry.Based on the above background,this paper clarifies the issues that need to be studied,namely,how to achieve accurate and objective risk assessment for online lending platform,explore the scientific construction of risk evaluation index system of online lending platform and the application of BP neural network to the risk evaluation of online lending platform.Sex.In the construction of the risk evaluation index system of the online loan platform,the current academic circles have not formed a unified construction rule.This paper draws on the ROCA rating method commonly used in the traditional banking industry,and combines the risk composition of the online loan platform to construct a risk evaluation index system for the online loan platform.The system consists of five primary indicators and 16 secondary indicators.The first-level indicators are risk control indicators,product and operation indicators,compliance indicators,asset quality indicators and user evaluation indicators.The set of indicators system combines the characteristics of online loans and covers the various factors of online loan risk formation.In the selection of the method model,traditional methods such as factor analysis and regression analysis used by previous scholars are not used,and the traditional methods are used to avoid more constraints.In the application,the strict assumptions must be met.BP neural network can be used.Non-linear mapping and machine learning algorithms with strong generalization ability.At the same time,the sample size is expanded,and the crawler technology is used to climb 240 data of the website of the online loan home.The BP neural network is used to evaluate the risk of the online loan platform.After the model training simulation,the data accuracy rate meets the expected requirements,which proves that the BP neural network is effective and convenient for the network loan platform risk assessment.It proves the effectiveness of the modern evaluation methods such as BP neural network applied to the online loan platform evaluation.Subsequent research provides a broader research perspective.In addition,this paper expounds the use scenarios of the risk assessment method in the perspective of the national regulatory layer,the platform itself and the investors,and puts forward corresponding suggestions for the management and risk identification of the online loan platform.