Developments over the past few years in predictive analytics are providing opportunities to improve the early identification of claims with a higher likelihood of piercing workers’ compensation reinsurance layers. 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.
While an excess loss is obvious for some catastrophic claims, the buildup to the attachment point is less obvious for many excess loss claims due to the subtleties of compounding factors. A significant challenge with early identification analytics for claims that have not reached an excess loss attachment point is that the administration of the claim is often handled by several specialists without any single participant noticing the aggregation of costly factors.
A recent development in predictive analytics is the use of machine learning software that extends the principles of conventional multivariate analyses. In contrast to the conventional analyses, these advanced analytic methods are not limited to linear relationships. Another development is the extraction of text information from claim adjusters’ notes, nurse care manager reports, and medical reports.
The advances with machine learning software and text mining algorithms are necessary tools for the early identification of claims most likely to become excess loss claims. To learn more about how analytics has affected the early identification of claims, read this article by Lori Julga and Phil Borba.