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May 31, 2026
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Abstract

Customer churn remains a critical strategic challenge for banking institutions operating in increasingly competitive digital markets. This study constructs and evaluates a Decision Tree classifier based on the Classification and Regression Trees (CART) algorithm to predict customer churn using a publicly available dataset of 10,000 banking customers distributed across three countries. A scikit-learn Pipeline integrating OneHotEncoder preprocessing and a CART-based classifier with Gini impurity criterion and constrained depth (max_depth = 5) was trained, evaluated, and critically interpreted. The model achieved near-perfect test-set performance, with accuracy of 99.85%, precision of 99.75%, recall of 99.51%, F1-score of 99.63%, and ROC-AUC of 99.75%, with a train-test performance gap below 0.2% across all metrics. Feature importance analysis, however, revealed that the Complain variable alone accounted for 99.80% of the model's Gini importance, indicating potential data leakage between the predictor and target variables. This finding constitutes the primary methodological contribution of the study: high classification performance is a necessary but insufficient criterion for validating a model's operational suitability, and feature importance auditing must be a mandatory component of any production-bound machine learning pipeline. Beyond the technical dimension, the study derives actionable managerial implications for banking customer retention, positioning real-time complaint monitoring as the most operationally significant early warning mechanism for churn risk.

Keywords

bank customer churn CART algorithm customer retention data leakage decision tree machine learning

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How to Cite
Muhammad Ramdan Alqadri, Ahmad Syarif Hidayatullah, Rizal Bakri, & Muh. Qardawi Hamzah. (2026). Decision Tree-Based Customer Churn Prediction in Banking: Predictive Performance, Data Leakage Detection, and Managerial Implications. Online Journal of Management, Innovation, Economics, and Digital Studies, 1(1), 1–15. Retrieved from https://onmind.bisdig.feb.unm.ac.id/article/view/636
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