用多层模型同时预测用户是否响应信用卡促销及违约风险。
A Multilayered Approach to Classifying Customer Responsiveness and Credit Risk
- 分三层建模：仅响应、仅风险、响应与风险联合预测。
- 随机森林在风险和联合模型中表现最佳，准确率达83.2%。
- 额外树分类器在识别潜在响应者上召回率最高，达79.1%。
本研究评估了多种分类器在三类模型中的表现：响应模型、风险模型以及响应-风险联合模型，用于信用卡邮件营销和违约预测。在响应模型中，额外树分类器的召回率最高，达79.1%，表明其在识别可能响应信用卡优惠的客户方面效果出色。在风险模型中，随机森林分类器的特异性达到84.1%，有助于精准识别违约可能性最低的客户。在多分类响应-风险模型中，随机森林分类器的准确率达到83.2%，说明其在同时区分潜在响应者与低风险用户方面具有高效性。本研究通过优化多个性能指标，解决信用风险与邮件响应性的实际业务问题。
原文摘要 · Abstract (English)
This study evaluates the performance of various classifiers in three distinct models: response, risk, and response-risk, concerning credit card mail campaigns and default prediction. In the response model, the Extra Trees classifier demonstrates the highest recall level (79.1%), emphasizing its effectiveness in identifying potential responders to targeted credit card offers. Conversely, in the risk model, the Random Forest classifier exhibits remarkable specificity of 84.1%, crucial for identifying customers least likely to default. Furthermore, in the multi-class response-risk model, the Random Forest classifier achieves the highest accuracy (83.2%), indicating its efficacy in discerning both potential responders to credit card mail campaign and low-risk credit card users. In this study, we optimized various performance metrics to solve a specific credit risk and mail responsiveness business problem.
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