A leading retail bank faced rising delinquencies across its loan portfolio. Collections teams struggled to distinguish between customers likely to self-cure, those who required engagement, and those where settlement offers were appropriate. Effort was spread thin, resulting in inefficient outreach, inconsistent customer experience, and missed recovery opportunities.
The challenge was to prioritize customers intelligently without increasing operational complexity. Decisions needed to guide where to engage, where to exercise restraint, and where settlement would be effective, while remaining usable by collections teams in day-to-day operations.
Dhurin designed a customer prioritization framework that segmented accounts based on roll-forward risk, engagement responsiveness, and settlement propensity. Rather than relying on static rules, the system continuously learned from customer behavior, payment patterns, and response outcomes. Prioritization signals were embedded directly into collections workflows, enabling differentiated treatment paths instead of one-size-fits-all follow-ups.
Settlement rates increased by approximately 50 percent, while collections efficiency improved significantly. Customer experience became more consistent and defensible, and the bank realized an incremental quarterly profit uplift of around ₹3.5 crore. Collections shifted from reactive recovery to measured, outcome-driven engagement aligned to customer behavior.
