Tag Archives: claims analytics

How can advancements in predictive analytics help identify reinsurance workers’ comp claims early?

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.

MillimanMAX, a Milliman predictive analytics platform, renamed “gradient A.I.” to reflect advanced analytic techniques

Milliman has announced the launch of gradient A.I. (formerly MillimanMAX), an advanced analytics platform that uncovers hidden patterns in big data in order to improve workers’ compensation claims management. The gradient A.I. platform is a transformative InsurTech technology built on the latest advanced techniques and artificial intelligence (A.I.), and delivers a daily decision support system (DSS) for insurers and self-insurers.

Milliman has been conducting research and development in the most advanced areas of artificial intelligence—also known as “deep learning”—for over five years, and the rebranding of gradient A.I. is a reflection of that enhanced experience. Our goal with gradient A.I. is to deliver the most actionable intelligence to our clients in the form of “decision support—and we’re pleased to note that so far clients have seen underwriting profit improvements of 3% to 5% and claim cost reductions in the neighborhood of 5% to 10%.

The key differentiator of gradient A.I. is its ability to identify relationships between structured and unstructured data, unlocking powerful and previously unknown information to deliver a competitive advantage to self-insured groups, carriers, and third-party administrators within the property and casualty (P&C) market. Additional product features include a custom data warehouse, easily identifiable and actionable risk drivers, dynamic reporting, and customizable reports and dashboard.

To learn more, click here.

Predictive modeling is making workers’ compensation medical costs more manageable

Some insurers and third-party administrators (TPAs) have recently begun to tackle rising medical costs by implementing claims predictive models. In their paper, “Making workers’ compensation medical costs more manageable,” Rong Yi and Steve DiCenso discuss how next-generation predictive models enable changes in claims strategies and culture.

The authors highlight a case study in their paper illustrating how one company was able to make changes in these areas and gain value from enhanced predictive models.

The key strategic changes outlined by the authors focus on:

• Identifying and estimating future high-cost claims earlier.
• Identifying and quantifying the cost (risk) drivers of these claims.
• Creating a focused, early-intervention medical management program to prevent adverse claim development.

A cost-effective approach to claims analytics

Over the last several years we’ve published several examples of how technology has led to breakthroughs in actuarial analysis, including articles on high-performance and cloud computing. Now a new article looks at how advances in the approach to and affordability of predictive analytics are allowing insurers to take a more innovative approach to how they manage their claims. As Ravi Kumar points out in “A cost-effective approach to casualty claims analytics,” more sophisticated claims analytics can have significant benefits for a property and casualty insurer and are now available to smaller companies.