Research On The Impact Of Big Data Credit Investigation On The Default Rate Of Credit Loans Of Small And Micro Enterprises

In recent years,small and micro enterprises have faced the slowdown of macroeconomic growth and the huge changes in the industry cycle,and the operational risk has intensified,resulting in the corresponding increase in the risk of the banking industry in the credit loan business of small and micro enterprises.In order to improve the efficiency and accuracy of risk control,reduce credit risk,and break through the limitations of traditional risk management,the banking industry is gradually trying to apply the big data credit reporting model to enhance the risk control ability,The effect of big data credit investigation on risk control in the credit field of small and micro enterprises deserves attention and research.With the rapid development of financial technology,the credit risk management model that relies on traditional credit data in the past has shown its limitations.Compared with traditional credit data,big data credit has more obvious advantages,mainly reflected in the expanding data dimension.In addition to traditional credit data,behavioral data and social data can also be used to reflect the repayment ability and willingness of users.On the other hand,the iteration of credit evaluation means is different from the previous subjective judgment or the combination of linear regression models.Big data credit investigation is more a fusion model using data mining technology and advanced algorithms,which is intelligent,automated and timely.Therefore,this thesis takes the default rate of credit loans of small and micro enterprises as the entry point,based on the perspective of mitigating information asymmetry and reducing transaction costs,Explore the mechanism and impact path of big data credit investigation on credit risk management of small and micro enterprises.Based on the data of 26425 small and micro enterprise credit loans from Bank S from June 1,2020 to August 31,2021,the big data model is further constructed through variable screening and sub-model construction,and compared with the traditional linear regression model of credit reporting,the empirical conclusion is that the increase in the breadth and depth of credit reporting data brought by big data credit reporting can improve the prediction level of the default rate of small and micro enterprises,Second,compared with traditional credit investigation,big data credit investigation has more advantages in data and model,and belongs to the innovation of credit investigation in the era of big data.In addition,through further analysis,it can be concluded that the banking industry has a more accurate assessment of the credit risk of small and micro enterprises,which can enhance the confidence of banks in the credit of small and micro enterprises,make it possible for small and micro enterprises to obtain banking loans,and meet the financing needs of small and micro enterprises.This study also hopes to play a role in how financial institutions conduct digital transformation,optimize risk control concepts,and improve the credit risk management ability of small and micro enterprises.