Customer Churn Prediction and Personalised Recommendations in Banking

In the contemporary landscape of fiercely competitive banking, where customer expectations are perpetually evolving amidst the digital banking revolution, the imperative task of addressing the pressing issue of customer attrition has emerged. This paper proposes a robust solution: customer churn prediction and personalized recommendations. Through a two-phase approach, banks can proactively forecast and mitigate customer departures by harnessing the power of advanced analytics. The first phase entails analyzing historical data to accurately predict churn, thereby facilitating the implementation of targeted initiatives aimed at enhancing customer experiences and curbing attrition rates. In the second phase, the focus shifts to personalized recommendations powered by Explainable AI (XAI), ensuring that the system’s decisions are transparent and understandable. By tailoring recommendations to individual attributes, this approach enhances the banking experience, driving higher customer retention rates. It underscores the importance of customer-centric strategies to adapt to industry dynamics, foster enduring relationships, and ensure long-term success through the retention of satisfied customers.