A large South Indian bank was experiencing early balance erosion and rising volatility across its CASA portfolio. Relationship and marketing teams relied on lagging indicators and intuition, often reacting after customer behavior had already shifted. There was no unified, data-driven way to identify which customers needed attention, when to intervene, or how to avoid over-communicating and fatiguing customers.
The bank needed to protect balances without increasing customer fatigue or operational noise. Decisions had to identify early churn risk, prioritize the right customers, and guide timely interventions that teams could execute confidently in day-to-day engagement.
Dhurin designed an early warning framework combining customer attributes, account history, and transactional behavior to surface early signals of balance fatigue and churn risk. Built in close collaboration with marketing, risk, and operations teams, the framework emphasized operational relevance over analytical novelty. Insights were embedded directly into dashboards and CRM workflows, enabling teams to act early rather than react late.
Teams were able to prioritize retention efforts, intervene earlier with at-risk customers, and reduce unnecessary outreach. Customer fatigue declined, balance protection improved, and confidence in retention decisions increased across teams. What began as an analytics initiative evolved into a continuously learning retention system that improved CASA stability in production.
