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 to Have Ready
Key Takeaways SR 26-2, issued jointly by the Federal Reserve, OCC and FDIC on April 17, 2026, replaced SR 11-7 and sets out a risk-based approach to model risk management. It is supervisory guidance that does not set enforceable standards, and it is expected to be most relevant to banks with over $30 billion in […]
Collections Optimization in Banking: Four Strategic Approaches Preventing 39-44% of Losses
The Four Pathways at a Glance Most banks act only after an account is 30, 60, or 90 days past due, which is the costliest point to intervene. Behavioral signals, declining transaction volume, changing payment patterns, falling engagement, appear weeks earlier, so predictive AI can flag an at-risk account 90-120 days before default. This post […]
Collections Paradox in AI for Banks: Why Prevention Beats Recovery 6-8x
The Prevention-Versus-Recovery Case in Brief The collections paradox is that a quarter of strong recovery numbers can signal a prevention failure, because every dollar spent recovering a delinquent account costs far more than preventing the delinquency. Behavioral signals predict delinquency 90-120 days before credit-bureau data flags it, which opens an intervention window while the customer […]
Why Demographics Fail Banking: The Behavioral Fingerprinting Alternative
Why Behavioral Beats Demographic Segmentation: The Short Version Demographic segmentation fails at credit risk because it measures stable attributes, age, income, geography, while the behaviors that predict repayment change continuously. Two people who look identical on demographics can behave in opposite ways. Behavioral segmentation reads transaction patterns, which predict what a customer will really do. […]
iTuring.ai helped a leading NBFC in India increase collections by 116%
iTuring.ai helped a leading insurance aggregator in the Middle East increase lead conversion by 1.92x
iTuring.ai helped a leading education lender in Africa increase onboarding rates by 14%
iTuring.ai helped a leading general insurer in North America improve claim settlement efficiency by 32%
