Research On Personal Credit Risk Assessment Of Commercial Banks Under The Background Of Big Data

In the era of rapid information iteration of commercial banks,the screening of key data will affect the credit risk assessment,while the traditional manual review and assessment is inefficient and will bring interference factors to the assessment,so that commercial banks bear more risks and pressure.At present,commercial banks mainly occupy market shares and obtain profits through operating risks.So how to do credit risk assessment is the key for commercial banks to stand out in the financial industry competition.In the era of big data,how to effectively,make full use of existing commercial Banks massive amounts of customer information and resources,and to gain more credit risk assessment data,use these data to estimate the risk evaluation,and achieve the purpose of control risk,reduce risk,analysis of commercial bank credit risk decision and risk security has important practical significance.At present,commercial banks mainly occupy market shares and obtain profits through operating risks.So how to do a good job in credit risk assessment is the key for commercial banks to stand out in the competition in the financial industry.At the same time,commercial banks are faced with the complex challenge of reducing the credit risk default rate through risk management.Based on the analysis and elaboration of the research status and theory of big data credit risk assessment,this paper analyzes the inconvenience and inefficiency of traditional commercial bank credit risk assessment by using correlation analysis method and theoretical elaboration.In credit risk assessment,the most commonly used method is the Logistic regression model based on statistical methods.Logistic regression is a comparison method with more than two conclusions.On this basis,using multiple linear regression analysis,the credit risk of the borrower is evaluated by "whether the borrower defaults" under various conditions.Correlation analysis and Logistic regression model were used to empirically analyze the relationship between variables,and Logistic regression was used to construct the most conducive to the credit risk assessment of commercial banks.The results show that using Logistic regression to construct credit risk assessment model of X commercial bank can effectively prevent the credit risk of commercial bank.Through empirical analysis,it is concluded that age,occupation,the number of loans on other online platforms,as well as residence,real estate,annual income and other direct indicators have a significant impact on the results of credit risk assessment.At the same time,credit evaluation methods in the era of big data are more feasible than traditional evaluation methods.