软件导刊2024，Vol.23Issue(7)：138-143,6.DOI:10.11907/rjdk.231548
基于SMOTEENN-XGBoost的信用卡风险客户预测
Prediction of Customers with Credit Card Risk Based on SMOTEENN-XGBoost
摘要
Abstract
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.关键词
信用卡风险预测/数据平衡/SMOTEENN/XGBoostKey words
credit card risk prediction/data balancing/SMOTEENN/XGBoost分类
信息技术与安全科学引用本文复制引用
田园,郭红烈,吉倩..基于SMOTEENN-XGBoost的信用卡风险客户预测[J].软件导刊,2024,23(7):138-143,6.基金项目
云南省基础研究计划面上项目(202201AT070189) （202201AT070189）
