Tag Archives: advanced computing

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.

To optimise financial decision-making, human-and-machine iterative process proves most successful

Milliman has announced that an innovative new study examining multi-criteria decision-making using an iterative process of advanced computing and human input has shown superior results in risk management when compared with machine algorithms or humans alone.

Using an illustrative example from the life insurance industry, the study looked at how optimisation techniques can be used to develop insights into drivers of economic capital within an internal model framework, and how to then use these insights for risk management decisions. The findings illustrate that advanced computing, visualisation, and complex systems-mining techniques that include expert input can deliver superior optimisation results when faced with multiple objectives and multiple constraints, which machine algorithms alone find challenging to resolve.

While not obvious at the outset, combining human input with advanced computer modeling allows domain experts to analyse results and elicit insights into features that subsequent iterations of a model should contain, thereby refining the process.

Milliman’s study employed the DACORD platform from DRTS, Ltd. to support its system-mining efforts. ‘Future states are unknown, involve human affairs and are therefore complex,’ says Jeff Allan, CEO of DRTS, Ltd. ‘Augmenting experts with the appropriate tools and processes can aid the reasoning and evaluation of a range of solutions.’

Adds Milliman’s Corey Grigg, ‘Looking toward the future, this sort of optimisation technique can extend to big data, simulations, and enhanced visualisation, ensuring that even as the complexity of our data and problems increases, experts can continue to add value.’

The results suggest a number of practical applications for enterprise risk management (ERM) in the insurance industry, including finding patterns in key risks driving capital losses and understanding diversification in order to enable quick judgements about the similarities and differences in the risk profiles of different portfolio elements.

Milliman’s Optimisation study was conducted in conjunction with Dr. Lucy Allan of University of Sheffield. To read the entire study, click here.