Understanding Customer Churn in Retail Banking through Explainable Predictive Analytics: Evidence of a Product Paradox

Patrick Ndabarishye; Ajay Kumar Singh

doi:10.62411/jcta.15870

Authors

Patrick Ndabarishye Jain University
Ajay Kumar Singh Jain University

DOI:

https://doi.org/10.62411/jcta.15870

Keywords:

Customer Churn, Explainable AI (XAI), Financial Analytics, Machine Learning, Predictive Analytics, Retail Banking, SHAP, Stacking Ensemble

Abstract

The retention of customers in the retail banking sector is a critical economic imperative; however, predictive modeling is frequently hindered by severe class imbalance and the “Black Box” nature of complex algorithms. This study proposes a Heterogeneous Stacking Ensemble framework integrating XGBoost, CatBoost, and Random Forest base learners with a Logistic Regression meta-learner to forecast customer attrition. To overcome the pervasive “Majority Class Bias,” we introduce a “Dual-Imbalance Defense” that synergizes the Synthetic Minority Over-sampling Technique (SMOTE) with algorithmic cost-sensitive penalization. Furthermore, moving beyond standard accuracy metrics, the framework mathematically derives a dynamic classification threshold to guarantee a strict 0.90 recall rate, actively optimizing the capture of at-risk capital. Model opacity is addressed through the integration of a SHapley Additive exPlanations (SHAP) TreeExplainer. This cooperative game theory approach provides localized, patient-level “Reason Codes” for regulatory compliance and reveals global systemic vulnerabilities, including non-linear drivers such as the “Product Paradox.” Achieving a 0.90 recall rate and an AUC of 0.8654, this framework provides a statistically robust and operationally transparent tool for targeted customer retention.

Author Biographies

Patrick Ndabarishye, Jain University

Department of Computer Science and Engineering, Jain University, Bangalore- 562112 India

Ajay Kumar Singh, Jain University

Department of Computer Science and Engineering, Jain University, Bangalore- 562112 India

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