AI Charged-Off Debt Recovery for US Banks: Improving ROI on Legacy Portfolios
TL;DR A charged-off portfolio on a US bank’s balance sheet has a specific financial character. The accounts have been written down. The provision has been taken. The loss is already on the books. Recovery at this stage is pure upside against a cost base that has already been absorbed. The standard model for working that […]
Credit Union Collections AI: Balancing Member Relationships and Recovery
TL;DR The member who is 45 days past due on their auto loan is also the member who has held a savings account since 1998. Their spouse has a mortgage with the credit union. Their two adult children opened their first checking accounts at the same branch three years ago. The family’s total deposit relationship […]
AI for Early Bucket Collections: How US Community Banks Reduce 30-60 DPD Loss
TL;DR A US community bank with a $180 million consumer loan portfolio sees between 800 and 1,200 accounts enter the 30-60 DPD bucket every month. The collections team has three full-time agents. With current tools and contact infrastructure, they can realistically work about 600 accounts in a month. The remaining 200 to 600 accounts carry […]
Propensity Scoring for South African Collections: A Practical Introduction
TL;DR A collections manager at a South African retailer credit provider works a 30-60 DPD portfolio of roughly 8,000 accounts every month. Her current system ranks them by outstanding balance. Her team contacts the highest balances first and works down the list. By month end they have reached about 60% of the accounts. The rest […]
Champion-Challenger Testing for SA Banks and Credit Providers
TL;DR A collections model has been running at a South African bank for ten months. It was built on 24 months of payment data, the bulk of it pre-pandemic. It performed well in the first two quarters after deployment. Recovery rates in the 30-60 DPD bucket improved. Cost per recovery came down. In the last […]
Real-Time Collections Decisioning for India’s Digital Lending Market
TL;DR At 2:17pm on the 5th of the month, a borrower receives a salary credit notification. Her personal loan EMI was due on the 1st. She is now four days past due. At the moment her salary lands, her propensity to pay is at its highest point for the rest of the month. She has […]
Collections AI for South African Credit Providers: How the Technology Works
TL;DR A collections supervisor at a South African credit provider starts most Monday mornings the same way. She opens the delinquency report. She looks at the accounts between 30 and 90 days past due. She divides them into calling batches for the week, organised by balance with the larger balances worked first. Her team moves […]
Propensity Modeling for NBFC Portfolio Collections
TL;DR Picture a collections team at an NBFC looking at a bucket of 15,000 personal loan accounts. All of them sit between 30 and 60 days past due. Every account carries the same DPD flag. But inside that bucket are three very different borrowers. The first missed a payment because salary was delayed by a […]
Champion-Challenger AI Testing for Indian NBFCs and Fintechs
TL;DR Every NBFC collections head reaches the same decision point eventually. The current AI model has been running for eight months. Recovery rates are acceptable. The data science team has built a new propensity model that, in backtesting on historical data, outperforms the current model by a meaningful margin across the 30-60 DPD bucket. The […]
Real-Time Credit Decisioning for US Collections Operations
TL;DR A borrower calls in on a Tuesday afternoon and makes a promise to pay by Friday. The collections agent logs the commitment. The account is flagged as PTP. Friday arrives. The payment does not post. Under a batch-processing collections system, that broken commitment sits unactioned until Monday morning when the overnight batch runs. By […]
