Description: Historically different approaches have been used by actuaries in modeling mortality improvement. The SOA has recently released a framework that practitioners can use to project mortality improvement, providing methodological consistency across a range of applications.

Description: It has been a little over a year since the release of the Society of Actuaries (SOA) Research Institute's Mortality Improvement Model, MIM-2021. Since then, waves of COVID-19 have continued to occur in the U.S. The uncertainty around the COVID-19 pandemic and the slowing of U.S. general population mortality improvement prior to the pandemic are just a few of the reasons that make developing MI projection scales difficult. Uncoverthe results from intercompany surveys on mortality improvement, including one that addresses COVID-19, discover insights from a mortality expert panel on COVID-19 and future mortality, and discuss enhancements to MIM-2021. By attending the session, you will: - Understand MIM-2021 and 2022 enhancements. - Understand SOA Research Institute activities around COVID-19 and impact on mortality improvement. - Apply research results and insights presented to daily work.

Description: What is a vitagion? Is there such thing as catastrophic longevity? What does a 1 in 200 year mortality loss event look like? How might actuaries use mortality scenarios in their practice? Explore how to move beyond simplistic stress testing with singular percentage shocks to building a robust set of information, derived from stochastically generated scenarios for longevity and excess mortality outcomes. This data can inform assumption setting, longevity swaps, capital levels, and risk management. These concepts apply to life insurers, pension actuaries, and reinsurers. By attending the session, you will: - Understand what is a loss exceedance curve is and how it may be useful to quantify tail risks. - Comprehend how to incorporate stochastic variability into longevity and excess mortality assumptions. - Find out what elements go into a stochastic prediction of extreme mortality events using nested models. - Learn how to add more depth to youractuarial assumptions.