Bank Customer Churn Prediction
A classification system that identifies banking customers at risk of churning, providing relationship managers with early warning signals and the feature-level insight needed to design effective retention interventions.
The Challenge
Customer acquisition in retail banking is significantly more expensive than retention, yet many institutions lack systematic early-warning capability for identifying at-risk customers. By the time a customer closes their account, the retention window has passed.
The challenge is not just predicting who will leave, but understanding why, so that relationship managers can tailor their intervention to the specific drivers of each customer's dissatisfaction.
Approach
Results
Analysed over 10,000 customer records spanning demographics, transaction history, and product holdings, building classification models that identified customers at elevated churn risk with sufficient lead time for intervention.
SHAP analysis revealed that the primary churn drivers varied significantly across customer segments, confirming that a one-size-fits-all retention approach would be ineffective. For some segments, product engagement was the dominant signal; for others, tenure and transaction frequency changes were more predictive. This justified personalised intervention strategies tailored to each segment's specific risk profile.
The model gives relationship managers something they did not have before: an evidence-based early warning with a clear explanation of why each customer is flagged, enabling targeted outreach before the customer has mentally disengaged.