Machine Learning–Based Customer Churn Prediction in Banking Using Feature Selection and Ensemble Models

Abstract

Customer churn is a big challenge in the banking industry as there are certain strategies need to retain a customer forever. There are different computational analyses and reports to estimate possible churn and modify service tactics accordingly, resulting in greater customer retention rates. This research has proposed a novel Hilbert-Schmidt Independence Criterion (HSIC) amidst other techniques for the selection of the intricate features for a robust predictive performance. Alongside, this study is examined with customized machine learning models and evaluated with major key evaluation metrics to checkmate its optimal performance. The dataset utilized is collected from Kaggle comprises of 3,380 entries 19 predictor variables. The customized bagging approach incorporated with the HSIC optimized features performed excellently, achieving an accuracy of 98.06% and an AUC value of 0.9984 while reducing training time by 19% compared to the whole feature set. Ensemble techniques consistently beat single classifiers across all feature subsets, and dimensionality reduction considerably accelerated training while retaining an accuracy loss of less than 1%. Overall, this proposed method is effective at predicting customer attrition risk, delivering useful information for financial organizations when developing customer retention strategies, allowing banks to better personalize service approaches to keep clients.