Research on personal credit risk evaluation based on XGBoost

Abstract

In the current research field of personal credit risk assessment, the existing characteristics and their combination of characteristics have little relationship with the borrower’s default status, and the explanatory ability is not strong. In this paper, feature selection is carried out by Logistc regression, and default discrimination is carried out by XGBoost, and personal credit risk evaluation model is constructed. In addition, 10000 pieces of real credit data of X Bank are demonstrated to test the effectiveness of the model. There are two main tasks in this paper. First, by comparing three feature selection methods, Logistc regression, AIC-Logistic regression and BIC-Logistic regression, the Logistc regression model with the best AUC and accuracy was selected to construct an index system of personal credit risk evaluation. /*Second, by comparing the performance evaluation indexes of XGBoost, decision tree and K-nearest neighbor classification algorithms, XGBoost is selected to build a personal credit risk evaluation model with good performance in AUC, accuracy and Type II error.