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

Description: The COVID-19 pandemic has been an acute reminder of the material impacts that pandemics can have on Life (re)insurance and society at large. Learn to describe the journey of a Life reinsurance practitioner to model and operationalize pandemic and infectious disease risk, why this is of importance to actuaries, and their risk management. It will consider how such models have been used, developments over time, and challenges in using and calibrating these models. The session will also provide perspectives from a pandemic model provider who will describe the fundamental metrics for pandemic loss distributions and how learnings from the COVID-19 pandemic can be used to quantify the potential impact of the next pandemic. Finally, dive into an outline of how biological principles can be applied to anticipating plausible pathogens causing the next pandemic.

Description: The Mortality Improvement subgroup of the American Academy of Actuaries’ Work Group and the Society of Actuaries’ Mortality and Longevity Oversight Advisory Council released a recommendation for a set of improvement factors that vary by gender and attained age to be used in conjunction with the 2008 Valuation Basic table or the 2015 Valuation Basic table for AG-38 purposes. This recommendation has been adopted by the NAIC’s Life Actuarial Task Force.

Description: Recent and potential near future extreme experiences are leading us to a new normal rather than back to the prior environment. Learn more about several examples of past Regime Changes along with a detailed discussion of how recent disruptions lead toward a persistent change in the business, social and political environment. Come develop an understanding of these drivers of change as well as the likely impacts on the Financial Services industry.

Description: This project models and forecasts premature cardiovascular mortality, with a focus on two main risk factors - obesity status and education attainment level. It investigates the role of obesity and education in determining age-sex-specific premature cardiovascular mortality.

Description: This report explores the behavioral factors influencing insurance purchase among Gen Z and Millennials for forward-looking purposes, leveraging a rich dataset spanning 22 markets across six continents.

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: Virtual - Session 2C: Innovative Care Models for Medicare & Medicaid Dually - Eligible Populations

Description: "This session will explore the critical challenges and solutions surrounding algorithmic fairness in the life insurance industry. The presenters will delve into the fundamental mechanisms of model bias, examining how predictive algorithms can yield outcomes that systematically discriminate against certain groups through flawed machine learning processes, erroneous assumptions, or biased data. Participants will gain insights into the various manifestations of bias in insurance models and understand their profound implications for both consumers and insurers. Real-world case studies and examples from the insurance industry will illustrate these concepts, while a discussion of recent regulatory developments will provide context for compliance requirements. This session is essential for actuaries, data scientists, and insurance professionals committed to developing and maintaining ethical, unbiased predictive models."