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

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.