AI-Driven Personalization in BNPL Collections Strategy

This case study outlines how a buy now, pay later (BNPL) fintech company implemented AI-driven personalization to enhance its collections strategy. The company, which partners with healthcare providers to offer flexible payment plans, faced increasing consumer expectations for personalized support throughout the repayment journey. To address this challenge, the firm adopted the Prodigal Intelligence Engine (PIE) and the proCollect solution, which enabled the lender to deliver tailored outreach strategies based on consumer behavior and preferences. The implementation resulted in a significant 11% reduction in roll rates and a 45% increase in promise-to-pay rates during calling campaigns. Additionally, the lender experienced a 33% lift in pre-charge-off recoveries compared to traditional methods. The case study details the operational changes made, including the integration of fragmented data and the optimization of outreach strategies to maximize collections while maintaining a positive consumer experience. The lender plans to expand the use of proCollect to manage all past-due accounts, solidifying its role in the collections process.