The personal credit default discrimination model based on DF21

Abstract

Background: The personal credit default discriminant measures the size of the credit default risk, which provides an essential decision-making basis for banks. Methods: This article constructs a three-stage default discriminant model based on the DF21. In the first stage, this article selects the feature combination. This article obtains the default prediction results by traversing the decision tree from 20 to 500 and the learning rate from 0.08 to 0.12 in XGBoost. Taking the lowest Type II error, the highest AUC and accuracy as the first, the second, and the third principles (TAA principle), respectively, this article infers the optimal parameter of decision tree and learning rate reversely and gets the feature importance. This article uses the forward selection method to determine the optimal feature combination according to the TAA principle. In the second stage, this article screens the base classifier for DF21. Considering the applicability of the classifier on different data sets, this article selects the classifier with the good classification performance as the base classifier on each data set. In the third stage, this article constructs the default discriminant model based on DF21. According to the idea that the combination of strong classifiers generates a stronger result, the four strong classifiers are used as the base classifier to improve the cascade structure of DF21. Results: Compared with the first stage, the Type II error (the proportion of the banks’ principal loss) dropped by 4.41%, 5.98%, and 13.00% in the Japanese, Australian, and German, respectively, which proves the effectiveness of DF21. Conclusion: DF21 is significantly better than other classifiers and other scholars’ models according to the TAA principle.