Improvements in Predictive Analytics Help With Early Identification of Workers Comp Reinsurance Claims

July 13, 2018 by Philip Borba and Lori Julga

The early identification of claims likely to pierce reinsurance retention levels has long been a challenge for primary insurers and reinsurers. The good news is that over the past decade or so, the field of claim analytics has moved from performing forensic work on closed claims to analytics that can identify at 60 days from the date of injury (or sooner) claims with a high likelihood of exceeding a retention level.Executive SummaryDevelopments in predictive analytics are helping early identification of claims that are likely to pierce workers compensation reinsurance layers, write Philip S. Borba and Lori Julga of Milliman.

Executive Summary

Developments in predictive analytics are helping early identification of claims that are likely to pierce workers compensation reinsurance layers, write Philip S. Borba and Lori Julga of Milliman.

The analytics are not limited to identifying claims piercing the excess layer but look to identify claims likely to get within 50 percent or even 30 percent of the retention limit.

While an excess loss is obvious for many catastrophic claims (e.g., serious burns, certain amputations for young workers), for many excess loss claims, the buildup to the attachment point is less obvious due to the subtleties of compounding factors. These factors may include subtle combinations in the demographics and medical experience that are not easily noticed.