Advanced Enterprise Banking Churn Intelligence System using Machine Learning, Explainable AI and Interactive Analytics

This research presents an advanced enterprise-grade Bank Customer Churn Intelligence System developed using Machine Learning, Explainable AI, and interactive business analytics. The platform integrates predictive modeling, SHAP-based explainability, executive KPI dashboards, AI retention recommendation systems, professional PDF reporting, and Streamlit cloud deployment. The proposed system evaluates multiple machine learning algorithms including Logistic Regression, Decision Tree, and Random Forest, with Random Forest selected as the final production model due to superior predictive capability and business applicability. The platform demonstrates enterprise-level customer retention intelligence, explainable banking analytics, proactive churn prediction, and professional business reporting capabilities.