Research On Personal Consumption Credit Risk Early Warning Of ZJ Bank Under The Background Of Big Data Credit Investigation

Under the guidance of "consumption upgrading" and "expanding domestic demand",advanced consumption has become a habitual consumption mode,which not only injects a strong driving force into the development of consumer credit of commercial banks,but also poses a challenge to the control of consumer credit risk of commercial banks.The development of big data and Internet technology has provided a lot of data and technical support for commercial banks to carry out personal credit risk business,and paved the data and technology for commercial banks to carry out personal risk early warning.How to effectively prevent personal consumption credit risk of commercial banks,how to do a good job in personal credit risk early warning,and how to solve the problems of inaccurate and untimely risk early warning of commercial banks are the main research purposes of this paper.This paper integrates the traditional personal credit investigation system and the Internet credit investigation system under the background of big data,combined with the current situation of consumer credit risk early warning of ZJ commercial bank,constructs the personal risk early warning system of ZJ bank,and establishes the personal credit risk early warning model of ZJ bank,which provides a certain reference for other commercial banks to solve this kind of risk early warning problem.Firstly,this paper summarises the theories and literature related to personal consumer credit and credit risk early warning to provide theories and methods for subsequent research.The second is to combine the traditional credit collection system and the big data credit collection system,using the AHP hierarchical analysis method,to construct a credit risk early warning indicator system of ZJ Bank containing three levels,including four primary indicators of personal characteristics factors,credit history,lending bank-related factors and risk information,as well as 14 secondary indicators such as age and monthly income,using the Delphi method to evaluate the indicators,and finally to obtain the weights of the indicators The final weight of the indicators is obtained.Based on the weights,the "risk warning threshold" is determined,and four warning intervals are set according to the different levels of risk warning,namely S<0.6294,0.6294≤ S < 0.8599,0.8599≤ S <0.9521,S ≥0.9521,which correspond to heavy warning,medium warning,light warning and no warning respectively.The four credit warning levels are heavy alarm,medium alarm,light alarm and no alarm.Then,the index weights and evaluation values obtained from the AHP were used as the input and output values of the BP neural network,and the model was trained and simulated using the neural network.Finally,the paper concludes with a summary of the whole paper and proposes suggested measures for risk early warning of ZJ Bank.The main conclusions of this paper are as follows:(1)In the index system constructed by big data credit investigation and traditional credit investigation,the weight value of monthly income in the index scoring system is the highest,and the information of breach of contract and violation of public security law ranks second.The proportion of customer relevance and the number of business handled by the bank ranked last.(2)Through the training and Simulation of ZJ bank’s actual customer data,it is proved that the risk early warning model based on AHP-BP can effectively and accurately carry out risk early warning.