Tag Archives: reserve analytics

Milliman wins three InsuranceERM Americas Awards for its insurtech products and risk consulting services

Milliman is pleased to announce that the firm has won three 2020 InsuranceERM Americas Awards for its insurtech products and risk consulting services. The goal of the IERM Americas Awards is to highlight enterprise risk management leaders in the Americas.

In the technology category, Milliman’s Nodal™ was named “Analytics Solution of the Year” while Milliman’s Arius Enterprise® won “Cloud Technology of the Year.” Awards in these categories recognize insurtech solutions that enable strategic decision-making and provide risk insights that can improve efficiency and operations for (re)insurers. For instance Nodal, Milliman’s predictive modelling platform for early claim intervention, uses machine learning to comb through unstructured data and identify high-risk claims, and has helped clients reduce claims severity by 15% and overall losses by 41%. Meanwhile, Milliman’s Arius, one of the industry’s most comprehensive reserve analysis solutions, has saved clients hundreds of hours per year while simultaneously improving analyses and governance over data and processes.

Milliman was also recognized with a 2020 InsuranceERM Americas Award for its risk consulting services, with three members of the firm’s Chicago Life ERM practice taking home “Risk Team of the Year.” Anthony Dardis, Ariel Weis, and Chloe Lau have, combined, over 60 years’ experience on both the assets and liabilities sides of life insurance, and are well-known for their benchmarking capabilities and “practical-first” approach to risk management.

“Transformation in the insurance industry begins by leveraging new technologies in conjunction with the latest data and expert analysis, three areas in which Milliman excels,” says Milliman President and CEO Steve White. “Managing complex risks requires both the best expertise and the most innovative tools, so we feel gratified to have won IERM Americas Awards for both our risk consulting and technology solutions.”

For more information on Milliman’s insurtech products, including Nodal and Arius, click here. For more information on Milliman’s enterprise risk management solutions, click here.

Beyond the familiar reserve analytic methods

The paid chain ladder and the incurred chain ladder are two of the most frequently used methods that actuaries employ to develop indicated loss reserves for property and casualty companies. Their popularity stems from both their ease of use and simple familiarity. However, there are other reserve analysis methods, such as Bornhuetter-Ferguson, that can prove extremely valuable.

In an article entitled “A fresh look at actuarial reserving methods,” Milliman’s Susan Forray discusses the results from a test of 30 less-common analytic methods. Here is an excerpt:

The results of the analysis were surprising. In brief, we found that the methods exhibiting the greatest skill over time were not the most popular but rather those that best satisfied the following two criteria:

1. They relied, at least in part, on case reserves in their evaluations.
2. The paid-to-date data they used did not directly influence the indicated unpaid loss.

We identified different methods satisfying both criteria, each of which exhibited greater skill than the incurred chain ladder – and significantly greater skill than the paid chain ladder.

…The results of our study suggest that there are many more valuable methods for reserve analysis beyond the incurred- and paid-chain-ladder methods, and that the paid chain ladder, in particular, should not receive the weight if often does. Of course, this is a general observation, and a particular company’s circumstances always should be considered in selecting methods for any reserve analysis.

…The ongoing challenge is identifying which new methods to select among the handful indicated and how to weight them against the more common methods already in our actuarial toolbox. This is an area for possible future work.

Whichever methods actuaries ultimately decide to use when performing reserve analyses, this study strongly suggests we should all consider methods beyond the familiar chain-ladder approach.

This article was first published in the May/June issue of Contingencies.