An Intelligent Banking Customer Churn Prediction and Lifetime Value Analytics System Using Machine Learning and Survival Models

The persistent challenge of banking customer churn imposes substantial revenue attrition on financial institutions operating within hyper-competitive digital environments. This paper presents Retain360, a comprehensive end-to-end analytical platform that addresses this challenge through the systematic integration of ensemble classification, survival analysis, and explainable artificial intelligence (XAI). The system is trained and evaluated on the IBM Banking Customer Churn Dataset, encompassing demographic, transactional, and service-usage attributes for 7,043 customer records. A Random Forest classifier, trained with class-balanced weighting and hyperparameter-optimised via Grid Search Cross-Validation, achieves an F1-Score of approximately 0.62 and a ROC-AUC of 0.85 on the heldout test partitiondemonstrating discriminative capability substantially exceeding random baselines and competitive with state-of-the-art benchmarks. The survival analysis component employs the lifelines library to fit Kaplan-Meier survival curves and a Cox Proportional Hazards (Cox PH) model, enabling the derivation of individualised hazard functions and survival probability trajectories over customer tenure. These temporally-resolved risk profiles underpin a personalised Customer Lifetime Value (CLTV) estimator that translates survival-derived expected tenure into quantitative revenue projections. Model interpretability is achieved through a tri-layer explainability framework comprising Permutation Importance for global feature ranking, Partial Dependence Plots (PDPs) for marginal effect visualisation, and SHAP (SHapley Additive exPlanations) force plots for instance-level prediction attribution. The complete system is operationalised as a Flask-based web application delivering real-time churn probability scores, risk gauges, SHAP explanations, and survival visualisations through a form-driven interface accessible to non-technical banking professionals. Retain360 thus bridges the methodological gap between academic machine learning research and actionable, production-grade customer retention intelligence.