Dynamic behavior analysis and ensemble learning for credit card attrition prediction

Abstract

Credit card attrition imposes a substantial business cost for financial institutions. Early and accurate prediction of customer churn allows banks to take proactive retention measures. However, modeling credit card attrition presents complex challenges given evolutionary customer spending behaviors. This paper puts forth a robust methodology harnessing dynamic behavior analysis along with ensemble learning to capture non-static patterns in transaction data. Explainability techniques further enable interpretation of attrition likelihood on an individual customer basis. Rigorous experiments demonstrate significant predictive performance improvements attained using the proposed approach.