A leading retail bank in the US transforms collections operations and decreases credit losses

The bank's existing legacy risk segmentation did not capture granularities in customer behavior and divided the customer base into four key segments only. Essentially, more customers were rolling from the 60-day overdue period to the 90-day one, thereby sharply affecting the roll rate percentages.

With a high number of contacts per customer, the cost of service was escalating. The bank was not effective in when to pitch what offers to delinquent customers. It was wasting resources and a number of man-hours in the sub-optimal process.

Our custom collection analytics solutions enabled Key-Performance-Indicator (KPI) trees to identify which customer segments had the maximum potential and provided opportunities in terms of credit loss and cost dollars. The solution involved the creation of risk mitigation strategies to minimize delinquency roll rate and designed risk triggers across 'Days Past Due' buckets.

The solution also entailed customer contact and offer strategies to cater to individual segments. This segmentation was done after analyzing the 'days past due' journey and which customers showed real potential for repayment. Not only did this maximize recovery rate, but also minimized the overall operational costs of the bank. AI/ML enabled insights vastly improved the bank's customer segmentation and increased the accuracy of its existing risk segments.

Our custom collection analytics solution transformed the bank's operations. The AI/ML-enabled insights vastly improved customer segmentation and risk assessment, leading to a significant decrease in credit losses and a more efficient collections process.