Subrogation is a critical part of claims management, but identifying opportunities can be tedious and time-consuming. Traditional processes rely on manually reviewing accident reports, statements, and notes to determine liability and potential recoveries. Shift’s AI automates this process by analyzing unstructured text, quickly assessing fault, and highlighting subrogation opportunities that might otherwise be missed.
Shift’s AI leverages GenAI to extract critical insights from first-person statements, adjuster notes, and other unstructured text. In this case, the system read the insured’s narrative, interpreted the sequence of events, and estimated liability, which automatically identified the other party as likely at fault. By combining liability assessment with subrogation rules, the AI highlighted opportunities for recovery that might have been overlooked in a manual process.
This automation enables insurers to process thousands of claims quickly, uncover hidden recoveries, and improve overall efficiency, while ensuring that valuable subrogation opportunities are never missed.
This video is part of a series of interesting cases presented by our insurance-focused team of data scientists. For more examples of Shift Technology's AI-driven results, browse the AI in Action library