Authors - Timothy T Adeliyi, Debajit Saikia Abstract - Banks rely heavily on long-term customer relationships to ensure sus-tainability, profitability, and competitive advantage. In an increasingly saturated financial services market, customer churn poses a significant threat to revenue stability. Artificial intelligence (AI) and machine learning (ML) have enhanced predictive capabilities in churn modelling; however, the increasing complexity of high-performing models often limits human interpretability and trust. This study investigates how predictive accuracy can be balanced with interpretability in credit card churn modelling through an explainable machine learning frame-work. A quantitative mono-method design was adopted using a publicly available credit card churn dataset comprising approximately 10,000 customer records. Following exploratory data analysis (EDA), multiple classification algorithms were implemented, including logistic regression, decision trees, k-nearest neigh-bours, support vector machines, gradient boosting, and random forests. The ran-dom forest model achieved the highest predictive performance (AUC = 0.940753) and was subsequently selected for interpretability analysis using Shap-ley Additive exPlanations (SHAP). The SHAP-based analysis enabled transpar-ent identification of feature importance and revealed the underlying drivers in-fluencing churn predictions. Graphical explanations were generated to enhance human understanding and support decision-making processes. The findings demonstrate that sustainable deployment of ML systems in banking requires a deliberate integration of predictive performance, domain knowledge, human-in-the-loop validation, and continuous monitoring. This study contributes to the dis-course on trustworthy AI in financial analytics by illustrating how interpretability techniques can strengthen confidence in high-performing churn prediction mod-els without compromising accuracy.
