Description: General Session VI

Description: Critical factors to consider as we age include where we live, who we interact with, how we get the help and support we need when we need it, and how our health care is organized and delivered. Underlying this is how we finance the cost of specialized housing, care and support. Some communities are age-friendly where we can obtain supportive services, others are not. Some people choose to live in senior housing which integrates support services, others choose to live in the community among people of all ages. Some health care systems are well designed to meet the needs of older people, others are not. The cost of living for older adults, considering all of these factors, varies by community. Explore more about the perspective of older adults, how communities have structured to meet their needs, and how society as a whole is becoming aware of and addressing a growing aging population.

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.

Description: "Population Aging, Global Climate Change and Social Security – Sally Shen, Research Associate, Global Risk Institute, John A. Turner, Ph.D., Director, Pension PolicyClimate Change and Mortality Time for Actuaries to Pay Attention – Sam Gutterman. FSA, CERA, FCAS, FCA, HONFIA, MAAA"

Description: Discover an introduction to decomposing differences in life expectancy by age and cause of death. Take a look into demographic techniques that decompose life expectancies at birth and older ages into the portions contributed by later age groups and causes of death-. These have been in existence for some time and provide a convenient device for analyzing mortality change over time as well as differences between sexes, countries, and ethnic groups. The methods will be described and illustrated by spreadsheet examples.

Description: This article highlights the findings of a recent SOA survey on accelerated underwriting (AU) practices, including the amount of AU business ceded, the results of three opinion questions, and an estimate of mortality slippage among AU programs.

Description: Mental health, suppoting policyholders

Description: Retirement planning generally includes a focus on decisions needed at time of retirement, but it often fails to focus on changes during retirement and additional decisions needed as people age. The 2021 SOA Risk Survey and other SOA research documents the lack of focus on future change. It is common for people to lose cognitive ability and generally to become less able to make decisions, manage their finances, their medical care and their lives at high ages.This panel focuses on communication and decision issues. The SOA has recently issued a decision guide on High Age Decisions. The brief identifies four domains for high age decisions: ·Health, health care and staying healthy ·Managing finances ·Housing and transportation ·Support networks The panel will include a discussion of communications and how we change, problems of physical, hearing, sight or cognitive decline and the challenges created and the four domains in the SOA Decision Brief.

Description: Come discuss how reinsurers can work side by side with insurer to better develop products, and lift in the areas that reinsurers have more experience, help matching products with distributions (or vice versa) and provide advanced technologies associated with tools and software. The speakers will also present Direct to Consumer plan from various points of view, i.e., distributors, carriers, and reinsurers. Some case studies will also be presented where traditional reinsurers and asset-intensive reinsurers helped insurers bring products to life.

Description: "On the decomposition of mortality models into causes of death – Andrés M Villegas Ph.D – UNSW Australia, Madhavi Bajekal, Ph.D.,HONFIA, University of London, UK, Steven Haberman, Ph.D. – Bayes Business School, University of London, UK Forecasting cause-specific mortality rates using the insights from the cointegration analysis, Séverine Arnold, PhD, Viktoriya Glushko, Department of Actuarial Science, Faculty of Business and Economics (HEC Lausanne), University of Lausanne, Switzerland"