Construction of SME Loan Default Risk Prediction System for Commercial Banks Using XGBoost Model

Abstract

Small and medium-sized enterprises play an important role in promoting employment and innovation, but their small scale, opaque financial information, and unstable operating conditions increase credit risk for commercial banks. Accurate prediction of SME loan default risk can reduce credit losses, optimize credit-resource allocation, and support sustainable SME development. Taking SME loan data from commercial banks as the research object, this paper constructs a loan default risk prediction system based on the XGBoost model. Key indicators affecting loan default are first identified through literature research and expert interviews, including enterprise financial indicators, non-financial indicators, and macroeconomic indicators. The collected data are then cleaned, missing values are processed, and feature engineering is conducted. The XGBoost model is constructed and optimized through grid search and cross-validation, and compared with logistic regression, random forest, and LightGBM models. Evaluation results based on confusion matrix, ROC curve, and AUC show that the XGBoost model achieves an AUC of 0.89 and maintains an AUC of 0.88 on the independent test set, indicating strong predictive accuracy, stability, and non-overfitting performance.