Description: This report, part of the Society of Actuaries [SOA] ‘Experience Studies – Business Practice Surveys,’ presents the results of a survey conducted by the SOA Committee on Life Insurance Mortality and Underwriting Surveys - Preferred Underwriting [Reinsurance] Survey Subcommittee. The purpose of this survey, the first preferred underwriting survey compiling reinsurance information from U.S. and Canadian reinsurers on U.S. Life business, was to obtain the opinions of the reinsurance market on preferred risk underwriting.

Description: This report, from the Society of Actuaries [SOA] ‘Experience Studies – Individual Annuities,’ consists of tables providing extensive results of the latest study of intercompany mortality experience under structured settlement annuities. The first study, published in the Transactions of the Society of Actuaries 1991-92 Reports, included experience through the calendar year 1989. The second study, published in the Transactions of the Society of Actuaries 1995-96 Reports, included experience through calendar year 1993. This study looks at experience through calendar year 1997.

Description: Donald A. McIsaac obituary.

Description: The 2001 CSO Preferred Class Structure Mortality Tables were developed at the request of the ACLI and adopted by the NAIC in September of 2006. Links to report and tables included.

Description: During this session, the candidates for SOA President-Elect briefly share information about themselves and their backgrounds, answer prepared questions and respond to member questions. This is your opportunity to learn more about the candidates for 2017-2018 SOA President-Elect.

Description: FAP module frequently asked questions

Description: Enterprise Risk Management (ERM) provides practitioners all of the tools necessary for assessing, quantifying, and managing risk on an aggregated, holistic basis. However, in practice it is often found that there is a tendency for company managements and even seasoned risk practitioners at times to navigate their risk exposures one at a time. This habitual approach produces sub-optimal results due to the oversimplification of risk events and understating the complexity of the world in which decision makers must navigate. At the heart of this sub-optimal behavior is the human predilection towards mental accounting. As with financial accounting, mental accounting employs a ledger function that mentally registers debts and balances. However, unlike a traditional ledger, the mental ledger is not precise, does not always sum debits to equal credits, and creates arbitrary categories for resources that are treated as independent, unrelated pools. These differences in function profoundly impact financial judgments and decision-making. Learn more about the long history of mental accounting, including examples from daily life to illustrate the principles involved. Discuss how mental accounting can arise during the ERM process cycle, with a special emphasis on its impacts on effectively quantifying risk and how this results in sub-optimal decision making. Discover behavioral tools specifically designed to mitigate the risk of mental accounting creeping into a company's ERM activities.

Description: In summer 2021, the SOA Research Institute’s Mortality and Longevity Strategic Research Program sponsored a webcast examining individual life and group life emerging mortality in a COVID-19 world. Since then, Delta and Omicron coronavirus variants have fueled waves of the virus in the U.S., boosting the total number of COVID-19 deaths since the beginning of the pandemic to near the 1 million mark. Hear an update and insights into how the life insured population is faring during COVID-19 by discussing the findings from two recent SOA Research Institute initiatives. LIMRA, Reinsurance Group of America (RGA), the Society of Actuaries (SOA) and TAI have collaborated to analyze the impact of COVID-19 on the individual life insurance industry’s mortality experience. Additionally, the SOA Research Institute has surveyed insurers to gather a high-level view of U.S. Group Term Life Insurance mortality results during the COVID-19 pandemic, comparing them to pre-pandemic mortality levels. The Group Term Life industry mortality results will be presented with monthly data through March 2022.

Description: The presenters in this session will highlight the SOA’s work on an updated Medicaid underwriting margin model. This research project includes the model along with a set of instructions and a paper describing the key components of underwriting margin, including cost of capital and risk margin. The model was created as a tool for Medicaid actuaries representing both MCO and State principals to determine appropriate underwriting margins components within Medicaid managed care rates. Understand a brief description of the motivation for and underlying theory behind the model. Walk through different model inputs and run through some realistic scenarios that Medicaid actuaries may encounter in where they operate. By attending this session you will be able to: • Understand the role that Medicaid underwriting margin plays in Medicaid rate setting. • Understand the key components of Medicaid underwriting margin. • Understand the factors that necessitate underwriting margins. • Understand how to incorporate appropriate Medicaid underwriting margin levels in Medicaid rate setting.

Description: Models are meant to represent reality, and they often do so quite well. However, they cannot make decisions that contradict the modeling algorithm or underlying assumptions and data. Neither do they possess emotional intelligence or the ability to adjust for social equality. As complexity of models evolved to predictive analytics and machine learning, reliance on model results has increased as well. Over the last decade, as actuaries, modelers and data scientists looked closer into their models, they often identified certain social biases in model assumptions and algorithms. From the use of indicators like credit scores to geo-social segmentation, insurance models have often skewed results in favor of certain socio-economic groups. Underlying this segmentation, one can identify bias around access to health care and education, socioeconomic groupings and financial literacy.