"Behavioral Analytics–Driven Customer Churn Prediction Using CatBoost"

Customer churn in the banking sector is represents a significant and quantifiable threat to revenue stability, institutional trust, and long-term sustainability. While conventional retention strategies predominantly rely on demographic profiling and account-level financial indicators, this study research advances a behavioral analytics paradigm that situates customer engagement depth and product utilization breadth at the center of churn prediction. Using a dataset of 10,000 European bank customers sourced from the European Banking Dataset (Simulated/Public Benchmark Dataset), we developed a comprehensive machine learning pipeline featuring 40+ engineered behavioral and financial features, multi-model evaluation across six classifiers, and a production-grade prediction system. Our champion model, CatBoost with an optimized decision threshold of 0.20, achieved an ROC-AUC of 0.866, an overall recall of 77.89%, a Macro F1 of 0.7295, and an accuracy of 79% on a held-out test set of 2,000 customers. The pipeline incorporates SMOTETomek for within-fold imbalance correction, isotonic probability calibration, and a custom threshold optimization protocol designed to prioritize churn recall under economic constraints in the banking sector . Key findings confirm that inactive, single-product, high-balance customers constitute the highest-risk segment (churn rate > 38%), whereas active multi-product holders exhibit churn rates as low as 8.2%. This study provides a deployable framework for engagement-driven retention strategy, cross-sell optimization, and loyalty program design.