In this video, see how we helped a top five US Property & Casualty insurer uncover hidden subrogation opportunities in challenging PIP claims. By leveraging advanced generative AI models, we identified a critical alert that enabled the insurer to recover payments that would typically be overlooked due to stringent legal criteria. Our innovative approach not only streamlined the evaluation process but also ensured accurate identification of fault, ultimately saving valuable time and enhancing recovery opportunities. Watch the video to see how our technology transforms the claims process and empowers insurers to maximize their recovery potential!
This video is part of a series of interesting cases, featuring several of our insurance-focused team of data scientists.
Benjamin Wonderlin, Data Scientist, USA
"Recently, we were working for a top five U.S. P&C insurer, and we came across a very interesting subrogation alert in the state of New York. What was interesting about this alert was that it was on a PIP payment or personal injury protection payment, which are very difficult to recover and subrogate on. The reason for this is that legally, carriers can only subrogate if a few somewhat complicated criteria are met.
Namely, one of the involved vehicles had to either exceed 6,500 pounds in weight or have been operating as a commercial or livery vehicle at the time of the accident. Because these criteria are difficult to verify at scale, carriers will often overlook recovery opportunities on PIP payments in New York and similar states. What Shift was able to do, and for this particular claim, was to come in and take the third party vehicle information on file and compare it against an external data source containing a list of registered rideshare vehicles in the state of New York.
In doing so, we automatically determined that the third party vehicle was a registered rideshare vehicle, meaning that the PIP payment on that claim had a reasonable chance of being able to be recovered, so long as that vehicle was operating as a rideshare or livery vehicle at the time of the accident. Because of this chance, the claim entered our standard detection pipeline, where our generative AI models and other methods, analyzed the texts associated with the claim and determined that the third party was also at fault for the accident.
This resulted in a subrogation alert that required minimal review, because the reviewer primarily had to go in and make sure that the third party vehicle was in fact operating as a commercial or livery vehicle at the time of the accident. Ultimately, I think this claim demonstrates what takes Shift to the next level and that, it combines, advanced subject knowledge and state of the art methods to identify subrogation opportunities that many teams would overlook."
