GenAI Meets Explainability: Turning Churn Predictions into Personalized Retention Strategies

Abstract

In an increasingly competitive financial landscape, retaining existing customers is widely acknowledged to be more costeffective than acquiring new ones. While artificial intelligence (AI)-based predictive models have achieved high accuracy in identifying customers at risk of churn, they often fail to provide actionable strategies for customer retention. This paper addresses this limitation by proposing a post modeling framework that translates churn predictions into business-oriented retention actions. Using supervised machine learning techniques on structured customer data—such as transactional and behavioral features—from the financial sector, we first develop a high-performance churn prediction model. We then employ explainability methods, notably SHAP (SHapley Additive exPlanations), to identify the key drivers of churn at both global and individual levels. These insights enable us to segment customers into interpretable profiles (e.g., price-sensitive, service-dissatisfied, inactive), each associated with specific churn triggers. To move beyond prediction and toward proactive intervention, we propose tailored retention strategies aligned with each segment’s churn rationale. Furthermore, we explore the integration of Generative AI (GenAI) to support the automatic generation of personalized messages and strategy suggestions, enhancing the decision-making process for financial institutions. The proposed methodology bridges the gap between churn prediction and business actionability, offering a data-driven approach to customer engagement. Our results demonstrate that such an approach not only deepens customer understanding but also significantly improves the effectiveness of targeted retention campaigns.