Description: This article provides a very high level overview of accelerated underwriting programs and their history in the US marketplace. It then discusses setting up an actuarial control cycle for such products and some items related to risk management that are helpful in this setting. Pros and cons of retrospective studies, random holdouts, targeted holdouts and post issue analysis are discussed. A mathematical example of how to estimate the mortality impact of such programs from early emerging experience is provided.

Description: The presenters will highlight differences in mortality that result from socioeconomic issues. Topics will include the differences in cause-specific mortality among different socioeconomic classes, trends in these gaps over time, and recommendations for reducing inequalities in life expectancy in the future.

Description: The session offers high-level initial steps to get started using R, as well as a list of helpful resources for both general data science and actuarial science with R. Attendees will learn about popular R packages to perform practical tasks such as importing, transforming and visualizing data, as well as packages commonly used by actuaries. You will also be introduced to the markdown package for creating reproducible and reusable analyses and workflows. By the end of the session, you will be familiar enough with using R and RStudio to begin incorporating these tools in your day-to-day work as an actuary

Description: Last year, the COVID pandemic significantly increased the pace of change in the life insurance industry, particularly in Accelerated Underwriting (AU). In the past, underwriting process changes occurred relatively infrequently and over a matter of years. Now, with new data sources and predictive models constantly evolving, the frequency of underwriting enhancements and decisioning changes, particularly in AU programs, is occurring every few months. With so much change occurring quickly, how can a company effectively monitor and understand the effects of these changes to make continuous adjustments with confidence and agility? Well-designed data visualizations allow a reviewer to navigate and comprehend detailed results quickly because our brains can process data in a picture format more than 15 times faster than text. Today’s advanced BI tools can make AU program monitoring more efficient and help companies adjust quickly to the results from program changes.

Description: This report examines the mortality of Canadian pensioners subdivided by pension level, using data from the Canada Pension Plan (CPP) and Québec Pension Plan (QPP)

Description: A data format for mortality tables along with applications.

Description: This article details different methods for calculating mortality impact associated with new underwriting programs. A common simplified calculation is compared and contrasted with two first principles approaches. The strengths and weaknesses of both approaches are discussed.

Description: Table of Contents to the 1951 Reports of Mortality and Morbidity.

Description: This paper compares two mortality projection models – the latest version of the CMI Mortality Projections Model and the latest version of MIM-2021.Both models were applied to data for England & Wales and the US, and the results were compared.

Description: The 2009 Estonia experience study includes both mortality and persistency studies of policies in force any time during the 2006-2008 study period. The study applies to the entire portfolios of the contributing companies and these companies represent the vast majority of the Estonia individual life insurance market. Separate studies were performed based upon policy counts and insurance in force. Actual mortality was compared to expected mortality based upon the SOA 1975-80 select and ultimate mortality table.