Predict Claims Subrogation

Insurance Claims Decrease Costs Executive Summary
Predict which claims are more likely to be successfully subrogated for both new and existing claims.
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Overview

Business Problem

In 2018, the U.S Department of Transportation reported 6,734,000 automobile accidents. Accidents can not only result in the tragic loss of lives but also lead to significant financial losses for both the insured and their insurance carriers. There are methods insurance carriers apply to minimize the losses they incur in cases where their insured are not at-fault for the ensuing accidents.

Claims subrogation is one such method that describes the legal process where an insurance carrier acts on behalf of their insured to pursue financial reparations for damages caused by a third party. The National Association of Subrogation Professionals estimates that automotive insurance carriers on average recover 27% of the claims incurred to their insured by a third party individual or insurance carrier.

However, it could be much more; identifying and processing claims to subrogate require intensive claims reviews that prevent insurance carriers from expanding their total subrogations. As the information claims officers receive during the first notice of loss often don’t provide the full picture, insurance carriers also face difficulty continuously revisiting cases to decipher whether new information coming in changes a claim’s subrogation potential.

Intelligent Solution

AI makes your claims officers more productive by helping them predict which claims are more likely to be successfully subrogated. It does so for both new claims coming in as well as old claims where subrogation opportunities may have been missed. If at the first notice of loss a claim is predicted to have low subrogation potential, then your insurance carrier can safely process the claim without risking losses down the road. However, claims with high subrogation potential should be assessed before being processed to prevent going to litigation if it turns out it can as a matter of fact be subrogated.

Unlike existing approaches where claims officers only have the opportunity to analyze claims during a single time period, AI will automatically update its predictions whenever new data about a claim is received. This increases the accuracy of the prediction and offers greater certainty that subrogation efforts will end in a positive payout. AI will maximize the returns generated by your claims officers or third party subrogation agencies by prioritizing the claims with the combination of having the highest values and highest subrogation potential. To streamline their efforts, AI will also inform your claims officers of the top reasons why a claim is likely to be subrogated so that they can personalize their approach to subrogating every claim.

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Insurance companies are using machine learning and AI to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from underwriting to marketing in order to make data-driven decisions to lead to increased profitability.