Bank Churn Prediction Using Machine Learning

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Customer churn prediction represents a critical challenge for the banking sector, with significant implications for revenue retention and customer relationship management. This study investigates the factors influencing churn behavior among clients of a European retail bank, utilizing a dataset comprising 10,000 customer records. We employ a comprehensive machine learning framework incorporating seven distinct algorithms: Logistic Regression (as baseline), K-Nearest Neighbors, Decision Tree, Random Forest, AdaBoost, XGBoost, and an Artificial Neural Network. The analysis includes feature engineering to identify key predictors of churn, encompassing demographic characteristics, account activity patterns, and service usage metrics. Our comparative approach examines how different algorithmic architectures capture the complex relationships underlying customer attrition. The study contributes to financial analytics literature by providing empirical insights into machine learning applications for churn prediction in the European banking context. Furthermore, the results offer practical value for bank managers seeking to develop targeted retention programs and optimize customer engagement strategies through data-driven decision making.