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.
Subcontractors benefit companies in various industries like utility and construction with specialized knowledge to ensure product quality at a lower cost. However, these companies need to consider the additional liability risks associated with a subcontractor’s work and safety. Careless subcontractors that do not obtain proper insurance coverage can increase a company’s liability and negatively affect its financial results.
What types of insurance coverage does a subcontractor need? What happens when a subcontractor is uninsured without a company’s knowledge? Milliman’s Rachel Soich provides the answers in her article “Subcontractors: How a common business practice could lead to a mountain of insurance costs.”
Milliman has announced that gradient A.I., a Milliman predictive analytics platform, now offers a professional employer organization (PEO)-specific solution for managing workers’ compensation risk. gradient A.I. is an advanced analytics and A.I. platform that uncovers hidden patterns in big data to deliver a daily decision support system (DSS) for insurers, self-insurers, and PEOs. It’s the first solution of its kind to be applied to PEO underwriting and claims management.
“Obtaining workers’ compensation insurance capacity has been historically difficult because of the lack of credible data to understand a PEO’s expected loss outcomes. Additionally, there were no formal pricing tools specific to the PEO community for use with any level of credibility—until gradient A.I. Pricing within a loss-sensitive environment can now be done with the science of Milliman combined with the instinct and intuition of the PEO,” says Paul Hughes, CEO of Libertate/RiskMD, an insurance agency/data analytics firm that specializes in providing coverage and consulting services to PEOs. “Within a policy term we can understand things like claims frequency and profitability, and we can get very good real-time month-to-month directional insight, in terms of here’s what you should have expected, here’s what happened, and as a result did we win or lose?”
gradient A.I., a transformational insurtech solution, aggregates client data from multiple sources, deposits it into a data warehouse, and normalizes the data in comprehensive data silos. “The uniqueness for PEOs and their service providers—and the power of gradient A.I.—emerges from the application of machine-learning capabilities on the PEOs’ data normalization,” says Stan Smith, a predictive analytics consultant and Milliman’s gradient A.I. practice leader. “With the gradient A.I. data warehouse, companies can reduce time, costs, and resources.”
For more on how gradient A.I. and Libertate brought predictive analytics solutions to PEOs, click here.
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.
The workers’ compensation environment in California has been surprisingly stable over the last several years. Despite this stability, workers’ compensation remains one of the most complex exposures for employers. This study by Milliman’s Richard Lord and Stephen Koca and Keenan’s Bill Poland and Daniel Mattioli provides fundamental healthcare industry benchmarks from which informed decisions related to managing workers’ compensation in California can be made.
Self-insureds that understand the factors used by actuaries to project workers’ compensation losses can better integrate them into their projection processes and benefit from insightful discussions with actuaries. Milliman consultants Carly Rowland and Richard Frese offer some perspective in the Business Insurance article “Ten considerations for projecting self-insured workers compensation losses.”