AI-Led Omnichannel Collections Strategy for Auto Lender

This case study details the implementation of an AI-led omnichannel collections strategy by a subprime auto lender aimed at reducing charge-offs and improving payment collections. The lender faced challenges with increasing delinquency rates and stretched agent resources, leading them to seek a modern approach to collections that prioritizes customer experience. By partnering with Prodigal, the lender utilized AI and machine learning models to analyze consumer data, including intent-to-pay and channel affinity, to create a comprehensive view of each consumer. The strategy included personalized outreach through various channels, optimized timing for contact, and the use of branded templates tailored to different consumer personas. As a result of these efforts, the lender reported an 8% increase in payments collected, a 23% reduction in cost-to-collect, and a significant boost in digital engagement and self-service payments. The lender's success led to the expansion of the AI strategy across their entire pre-charge-off portfolio.