Can AI make debt collections less about chasing and more about predicting?

Predictive analytics can aid in identifying borrowers likely to default, but predicting individual defaults remains impossible. Lenders need to operate under the understanding that uncertainty is inherent in lending. The key is designing machines to function effectively within this uncertainty. Larger cohorts enable better statistical predictions of default rates, while smaller cohorts of individual borrowers yield less certainty.

Factors like macro events (e.g., Ganesh Chaturthi) and micro circumstances (e.g., delayed salaries) can impact repayment behaviors. Predictive analytics helps lenders allocate resources more intelligently, focusing on groups at higher risk and determining appropriate intervention strategies. The real potential for AI lies in response optimization rather than default prediction.

It should prioritize relevant interventions over broad chasing efforts. Combining predictive analytics with AI-led decisioning and communication can make a significant difference, especially when credit is extended to millions of borrowers. The focus should be on efficiently managing the inevitable defaults and making each interaction as pertinent as possible, leveraging data to inform decisions while preserving human judgement for nuanced situations.

Predicting defaults before they occur is unattainable, but making interventions more targeted can provide a competitive edge.