Prediction of Customers with Credit Card Risk Based on SMOTEENN-XGBoost
To achieve risk management for credit cards and reduce economic losses caused by credit card defaults,it is particularly important to develop an effective credit card risk prediction model.In response to the issue of imbalanced credit card data distribution,the ENN algo-rithm was used to improve the classical SMOTE algorithm,resulting in the construction of a credit card risk prediction model based on SMO-TEENN-XGBoost.Empirical evidence reveals that this model achieves a prediction accuracy of 91.8%and an AUPRC value of 0.903,which is significantly better than classical models such as SVC,GBDT,and AdaBoost.It holds significant value in predicting high-risk credit card users and aiding banks in accurately identifying customer credit risks.
国家科技期刊平台
NSTL
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