Description: This report presents research into recent population mortality experience compiled by the Retirement Plans Experience Committee (RPEC) of the Society of Actuaries Research Institute as it pertains to development of mortality improvement assumptions for measuring obligations of retirement programs in the United States.

Description: This report evaluates the long-term mortality consequences of the COVID-19 pandemic from a cohort-based perspective using historical mortality data.

Description: This report outlines a methodology for estimating mortality improvement factors based on data from insured lives. The report details challenges, methodology, results, comparisons with population data, limitations, and potential improvements.

Description: This report provides insights into statistical methods for imputing race and ethnicity in the context of insurance industry disparities. It discusses various imputation techniques, performance metrics, and the evolution of methods over time. The aim is to address the lack of consistent data collection on race and ethnicity in insurance datasets.

Description: In the first part of this series, the presenters will provide the results of a recent SOA multi-country survey on mortality improvement practices of life insurance companies. In the second part of this session, they will discuss the issues and solutions related to the development of the most recent RPEC mortality improvement assumptions.

Description: The document is a report on U.S. Population Mortality Observations updated with 2022 experience by R. Jerome Holman, FSA, MAAA. It covers mortality trends, analyses by factors like sex, age group, and cause of death, and includes methodological details for the data presented.

Description: Mortality Improvement is fundamental to setting your mortality assumptions. Creating your own Mortality Improvement assumption may seem like a Herculean task. The truth is creating a mortality improvement assumption with tools provided by the SOA, or the Continuous Mortality Investigation (CMI) make the process easy. Leaving you with the Herculean task of deciding if the assumption you created fits your product. Expect to learn about the CMI tool and the SOA's Mortality Improvement Model (MIM) tool, how you might get data to use in those tools, and what assumptions you need to make outside of data like what will the MI be in 20 or 30 years? Whether it is the general population, a segment of the general population, or certain causes of death, you will get very different answers.