Predictive Modeling of Bank Customer Churn: A Machine Learning Perspective

The act whereby customers break their relationships with a bank is known as bank customer churn, which brings difficulties for financial institutions. In this study, a comprehensive predictive modeling framework utilizing machine learning techniques is presented to anticipate bank customer churn. The primary objective is to leverage advanced methodologies to effectively predict churn behavior and subsequently enable proactive retention strategies. To address the class imbalance inherent in churn prediction tasks, Synthetic Minority Oversampling Technique (SMOTE) analysis is employed, enhancing the performance of predictive models. In addition, categorical feature analysis is conducted using Cramer's V statistic to discern the relationships between categorical variables and churn. Furthermore, the KruskalWallis <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$H$</tex> test is applied to assess the significance of numerical features in predicting churn behavior. Moreover, outlier detection and cleaning techniques are implemented to improve the robustness of predictive models. Subsequently, three powerful ensemble learning algorithms, namely Random Forest, Gradient Boosting, and eXtreme Gradient Boosting (XGBoost) are employed to construct predictive models for bank customer churn. The experimental analysis based on the actual data from banks as well as the comparison with existing approaches proves the feasibility of the presented approach in terms of accurate prediction bank customer churn. Comparative analysis of the performance of the Random Forest, Gradient Boosting, and XGBoost models showcases their respective strengths in capturing diverse patterns of churn behavior.