Scholarlink

<p>To address class imbalance and distribution shifts in bank marketing tasks, this paper presents an approach based on CatBoost ensemble and explainable AI, referred to as CBE-XAI (CatBoost Ensemble with eXplainable AI), to predict term deposit subscription intentions. The approach integrates five heterogeneous CatBoost base learners using a post-training dynamic weighting mechanism based on validation performance. It also employs a hierarchical SHapley Additive exPlanations (SHAP) system to aggregate local attributions for micro-to-macro feature importance analysis. In addition, the approach includes a pre-deployment adaptive fine-tuning (AFT) strategy with a composite loss function for cross-environment model calibration. Experimental results show that CBE-XAI achieves an Area under the receiver operating characteristic curve (AUROC) of 0.949 and an F1-Score of 0.621 on public datasets, demonstrating performance comparable to several standard benchmarks. External validation on a Chinese city commercial bank dataset confirms that the AFT strategy improves AUROC from 0.842 to 0.931 <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\:(p&lt;0.001)\)</EquationSource></InlineEquation>, enhancing cross-domain generalization. Furthermore, hierarchical SHAP analysis reveals key decision pathways for features such as call duration, ensuring consistency with banking business logic. CBE-XAI provides a potential technical solution for balancing accuracy, interpretability, and generalization in bank marketing.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Using ensemble learning and explainable AI to predict bank marketing customer subscription

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月19日

查看全文
  • Wenyue Wang,
  • Qi Liu,
  • Sufeng Li,
  • Hongyan Feng

摘要

To address class imbalance and distribution shifts in bank marketing tasks, this paper presents an approach based on CatBoost ensemble and explainable AI, referred to as CBE-XAI (CatBoost Ensemble with eXplainable AI), to predict term deposit subscription intentions. The approach integrates five heterogeneous CatBoost base learners using a post-training dynamic weighting mechanism based on validation performance. It also employs a hierarchical SHapley Additive exPlanations (SHAP) system to aggregate local attributions for micro-to-macro feature importance analysis. In addition, the approach includes a pre-deployment adaptive fine-tuning (AFT) strategy with a composite loss function for cross-environment model calibration. Experimental results show that CBE-XAI achieves an Area under the receiver operating characteristic curve (AUROC) of 0.949 and an F1-Score of 0.621 on public datasets, demonstrating performance comparable to several standard benchmarks. External validation on a Chinese city commercial bank dataset confirms that the AFT strategy improves AUROC from 0.842 to 0.931 \(\:(p<0.001)\), enhancing cross-domain generalization. Furthermore, hierarchical SHAP analysis reveals key decision pathways for features such as call duration, ensuring consistency with banking business logic. CBE-XAI provides a potential technical solution for balancing accuracy, interpretability, and generalization in bank marketing.