Debt Recovery Software for Indian Banks: Cutting Cost-Per-Contact While Staying RBI-Compliant
TL;DR Take a collections division running 50 agents managing a mid-size NPA book. Each agent makes 80 to 100 call attempts per day and achieves 35 to 55 connected calls. Fully loaded per agent, including salary, provident fund, training, floor space, supervision, attrition replacement, and compliance overhead, the monthly cost runs Rs. 35,000 to 65,000 […]
Banking AI platform in India: How to Evaluate AI Platforms for Bank and NBFC Collections in 2026
TL;DR Every major global Banking AI platform vendor now has an India page on their website. It mentions RBI. It shows the word “vernacular.” It references NBFC. Some have a case study featuring a bank you have never heard of. This is the problem. The Indian Banking AI platform india market attracted significant global attention […]
NPA Recovery Software: How Indian Banks and NBFCs Are Using AI to Reduce NPA Ratios in 2026
TL;DR India’s banking sector just achieved something remarkable. The gross NPA ratio of scheduled commercial banks fell to 2.1 percent by end-September 2025, a level not seen in decades. Public sector banks, which have carried the heaviest NPA burden since the 2015-16 stress cycle, reduced their ratio to 2.6 percent in FY25 from 3.5 percent […]
Model Risk Management for AI Collections: The Framework US Banks Are Missing
TL;DR Your bank almost certainly has a model risk management program. Your model risk management program was almost certainly built around credit scoring models. Those two facts together describe the compliance gap that is showing up in AI model examinations across US banking in 2025 and 2026. The model risk those collections AI systems carry […]
Building a Propensity Model for Collections: How US Banks Predict Payment Behaviour Before Default
TL;DR Once an account transitions to default, the probability of curing it drops to 7%. That single figure, drawn from research on consumer credit default transitions, is the entire business case for propensity modelling in collections. The probability of a current account transitioning to default sits at 23%. The probability of recovering an account that […]
AI Governance Monitoring for AI Collections Models: The Framework US Banks Need in 2026
TL;DR The OCC examiner’s question takes four words. “Show me your monitoring.” Not “walk me through your governance framework.” Not “do you have a validation policy.” Four words, and the answer either exists in documented, operational form or it does not. In 2025 and 2026, that question has become the single most consequential moment in […]
OCC AI Collections Audit: Nine Artifacts Examiners Demand Today
Key Takeaways Regulators now apply SR 11-7 model risk management guidance to AI collections systems with the same rigor they use for credit risk models. Banks using AI for debt collection face a new reality. Saying “the model works” no longer satisfies compliance requirements. Examiners want nine specific artifacts documenting every aspect of how you […]
Collections Optimization in Banking: Four Strategic Approaches Preventing 39-44% of Losses
Key Takeaways Most banks wait until accounts hit 30, 60, or 90 days past due before taking action on collections. This reactive approach costs them dearly in both recovery rates and operational efficiency. There’s a better way. Banks using predictive AI to identify at-risk accounts 90-120 days before they default achieve 39-44% higher collection rates […]
Collections Paradox in AI for Banks: Why Prevention Beats Recovery 6-8x
Key Takeaways Your collections team recovered 44% more debt this quarter. Congratulations. You also proved your bank failed at prevention. For Chief Risk Officers this collection paradox reveals fundamental misalignment in how banks measure success. Every dollar spent on collections costs six to eight dollars more than preventing the delinquency in the first place. Yet […]
Why Demographics Fail Banking: The Behavioral Fingerprinting Alternative
Key Takeaways Banks lose millions targeting customers based on who they are rather than how they behave. Demographic segmentation measures stable attributes that correlate weakly with dynamic payment behaviors. Age, income, and geography describe customers. Transaction patterns predict what they actually do. The shift from demographic to behavioral segmentation rests on understanding why demographic prediction […]
