Customer Churn Prediction in Banking–Machine Learning Application

This study applies machine learning to predict customer churn in commercial banking, using a publicly available dataset of 10,000 customers from Kaggle, comprising 14 demographic, behavioural and account-level features with an approximate 4:1 class imbalance ratio between nonchurners and churners. The 7 classifiers (logistic regression, decision tree, random forest, support vector machine, K-nearest neighbours, LightGBM and XGBoost) were tested on five performance metrics: precision, recall, F1-score, AUC-ROC and MCC. To address class imbalance, we applied SMOTE + ENN to the training set following data partitioning, while the test set retained its original distribution for unbiased evaluation. The optimisation of hyperparameters was done using Grid Search with stratified cross-validation. In addition to predictive benchmarking, this study proposes a three-level business analytics framework: Layer 1 filters significant churn predictors using permutation feature importance; Layer 2 utilizes K-means to categorize users into interpretable risk tiers; and Layer 3 leverages a bank-specific cost matrix to identify the optimal threshold that maximizes net financial value. Random forest outperformed other classifiers, achieving the highest AUC-ROC, F1-score and MCC, potentially the most effective model for customer churn prediction. The proposed framework provides a pipeline for commercial banks to move from a reactive to a proactive customer retention. Moreover, it provides an interpretable model and is both computationally efficient and practical, given the operational constraints of commercial banking. This framework can be extended in future work by leveraging longitudinal data at the transaction level with sequential techniques for better prediction regarding “when” customers actually churn.