Description: This research report that provides a literature survey of methodologies applying machine learning to insurance claim modeling.

Description: Back in the day, when data was scarce and computers were slow, actuaries were forced to use simple methods to account for risk. By far, the most common method was to add a margin, usually 5% to a “best estimate” calculation. Remarkedly, this method still works quite well in the aggregate. The problem is that, in many cases, decisions today require a more in-depth analysis. For example, how big a risk is a health plan really taking if it lowers its premiums by 10% to attract more members? This session will discuss techniques for measuring risk in today’s environment. Speakers will also share the results from the 5/50 research project, which forms the foundation for the techniques.

Description: Essays advocating for incentive compensation linked to the level of risk in an organization.

Description: After a very challenging year, actuaries need to look back and consider the lessons learned. Have we returned to where we were, or have we moved on to a new norm? What does the COVID-19 pandemic market data tell us that will help us to prepare for future global crises?

Description: This research report models the impact on the U.S. insurance market resulting from a post-Brexit economy in the UK.

Description: Newly released spring 2019 data added to the Actuaries Climate Index show that more frequent extreme weather and increases in sea level continue to push the index upward. The ACI’s five-year moving average now stands at 1.16, up from 1.12 last quarter.

Description: This research report is on the incorporation of flood and other catastrophe model results into pricing and underwriting.