Bank Customer Churn Prediction Using CTGAN-Augmented Data and Boosting-Based Ensemble Learning with SHAP Explainable AI

Abstract

Customer churn prediction remains a fundamental concern in the banking domain due to its direct impact on revenue stability and long-term customer value. A key challenge in churn modeling lies in severe class imbalance, which often limits model sensitivity toward minority churn cases. This study aims to develop an integrated and explainable churn prediction framework that effectively addresses class imbalance while maintaining robust predictive performance and interpretability. The proposed approach employs Conditional Tabular Generative Adversarial Networks (CTGAN), comparison of five boosting-based ensemble learning, and SHapley Additive exPlanations (SHAP) to preserve model interpretability. CTGAN is leveraged to synthesize high-fidelity instances for the churn class, yielding a class-balanced dataset that retains intricate tabular feature distributions. Five boosting-based ensemble models, XGBoost, CatBoost, Gradient Boosting Machine (GBM), Stochastic Gradient Boosting (SGB), and LightGBM, are systematically tuned using randomized hyperparameter optimization and evaluated under consistent experimental settings. Model performance is assessed using accuracy, precision, recall, and F1-score to capture classification performance under class imbalance. To ensure transparency, SHAP is applied to analyze global feature importance influencing churn predictions. Experimental results indicate CTGAN enhances model learning stability and detection capability. Among the evaluated models, CatBoost achieves the best results, with an accuracy of 0.9748 and an F1-score of 0.9178. The explainability analysis reveals that transactional features play a dominant role in churn. The novelty of this study lies in a unified and explainable churn prediction framework that integrates CTGAN-data augmentation, boosting ensembles, and interpretability for robust decision support in banking analytics.