Abstract
At present, China's Internet finance has flourished, showing a variety of business models and operating mechanisms. Through Internet technology, financial institutions can speed up business processing and bring users a better service experience. However, there are also problems such as credit risk and user fraud, and it is urgent to improve the level of risk control through credit scoring models. Because of this, this article uses the borrower data of a Chinese financial institution from January 2017 to June 2017 as the original data, and then uses the Spearman rank correlation test to screen out the variables with reliable explanatory power from the many variables of the sample data, and then Based on the variables selected, R 3.4.3 and SPSS 23.0 were used to construct a random forest model, discriminant analysis model, and logistic regression model. In general, different models perform differently under different sample characteristics, but the discriminant analysis has been better applicable. This paper compares the judgment accuracy of these three types of models and tries to establish a more effective financial credit scoring method, to solve the problem of constructing China's credit scoring system model under the current Internet financial background.
Highlights
1.1 Research Background The objective, comprehensive, and accurate individual credit rating model is an essential component of the personal credit rating system (Hand & Henley, 1997)
Based on abundant personal credit history and credit behavior data, the credit behavior pattern obtained by adopting the data mining method can more accurately predict the future credit performance of individuals, improve the efficiency of operation, reduce the cost of credit granting, and accurately estimate the risk of consumer credit, which is an essential tool for the internal scoring of financial institutions (Hsieh & Hung, 2010)
FICO scores constructed with discriminant analysis as the core are widely used in the field of credit scoring by Chen & Chen (2010) used the latest semi-supervised nonparametric discriminant analysis (SNDA), sparse tensor discriminant analysis (STDA), semi-supervised discriminant analysis (SDA), sparse discriminant analysis (Sparse DA), Fisher discriminant analysis (FDA), and multivariate discriminant analysis (MDA) to construct credit score models, respectively, and the results showed that SNDA, STDA, and SDA performed better than other discriminant analyses
Summary
1.1 Research Background The objective, comprehensive, and accurate individual credit rating model is an essential component of the personal credit rating system (Hand & Henley, 1997). 4. Screening of Explanatory Variables for Samples with Agents 4.1 Correlation of Explanatory Variables with Explanatory Variables In this chapter, 14 variables are selected to research the influencing factors of the borrower's deferred repayment, which are loan grade (x1), agent (x2), local nationality (x3), education level (x5), marital status (x6), salary (x7), fund availability (x8), gender (x9), provincial gross product (x10), per capita disposable income (x11), per capita consumption expenditure (x12), regional fixed investment (x13), regional fixed investment index (x14), unemployment rate (x15). The variables removed in turn are x6 (marital status), x7 (salary), x15 (unemployment rate), x3 (whether local), x13 (regional fixed asset investment), x11 (per capita disposable income), x12 (per capita consumption expenditure), x14 (regional fixed-asset investment index), x2 (agent), x1 (loan grade), the corresponding accuracy rate is shown in the table 10 below:.
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