A US based Auto Insurer wanted to gain a better understanding of who would have the ability to pay during a subrogation process.
The objective was to better focus their efforts in recovering claim payments.
By combining the Powerlytics Data Platform with the Auto Carrier’s data, Powerlytics was able to build a “likely to pay” score to help identify the consumers that had the greatest propensity of payment.
Likelihood to Pay Score Development Process:
The “likely to pay” score identified a meaningful group with a 75%+ likelihood to pay. As a result, the Insurer could leverage data to improve subrogated claims reserve forecasting and better focus its resources to both improve collections and reduce labor costs.