Description: "Establishing Methods for Annual Updates to Cohort Life Expectancies – R. Dale Hall, FSA, CERA, MAAAManaging Director of Research, Society of Actuaries Research Institute Longevity Disparity across Communities in the United States – Lijia Guo, Ph.D., ASA, MAAA , US Government Accountability Office, Applied Research and Methods Division, Washington DC"

Description: What are the recent trends in mortality? Should we expect different mortality experience in a post-COVID-19 world? Join us as we review the latest emerging trends in U.S. population mortality.by the end of this session, you will understand the impact of COVID-19 on future mortality experience. You will also have an awareness of other major causes of mortality and how they are trending.

Description: Hear about some applications using quantile regression-derived individual and group claim distributions and how they may be used in various traditional health care modeling situations. The session will not focus on the technical use of quantile regression to obtain estimates of claim distributions, but more so is intended to focus on the uses of the distributions in actuarial modeling and the additive framework in assessing claims distributions. Presenters will show how actuaries can better assess risks by focusing on the individual- and group-specific claims distributions as opposed to traditional point-estimate-centric models.

Description: With mortality rates significantly higher in 2020 and 2021 due to the COVID-19 pandemic, actuaries that rely on historical experience cannot properly craft assumptions about the future due to the recent excess deaths. This webcast focuses on the practicality of using pandemic mortality experience, as well as how to overcome the practical challenges of that data implementation. Explore an overview of different approaches used by institutions in the U.S., Canada and UK and an observation of industry survey responses. There will also be a comparison to recent SOA expert opinion surveys and short term impacts on the future of U.S. mortality.

Description: With large public filers having adopted LDTI in 2023, company practices are continuing to evolve as it relates to experience monitoring, assumption unlocking, as well as both internal and external communication and disclosures related to assumptions. In this diverse session featuring representatives from both insurance companies as well as industry analysts, we'll explore how companies approach emerging experience in a given year through a case study approach. The moderator will explore various topics such as off-cycle experience monitoring, experience outliers, simplifications and approximations, and materiality considerations. Each 'what-if' scenario will start with a prompt from the moderator, discussion among the industry panelists as to how they would approach the situation within their own organization, followed by commentary from the analysts as to how they might expect companies to respond in each case. Audience participation will be encouraged to explore potential alternative approaches in an engaging, interactive session.

Description: This session will focus on the introduction to Medicare Research Identifiable File (RIF) data and how you can turbocharge strategic decision-making for Medicare stakeholders. Through real-world examples, we unravel the practical applications of this data for plan sponsors, pharmacies, pharmacy benefit managers (PBMs), and drug manufacturers.

Description: This session will present three utilized underwriting models that can be used for mid-size employer group medical renewals. Key assumptions will be discussed including a significant focus on the stop loss risk since that is the major insured risk.

Description: Mortality Model Selection and Combination in RAlternative approaches for selecting mortality models and forecasting mortality have been proposed. The usual practice is to base mortality forecasts on a single mortality model selected using in-sample goodness-of-fit measures. However, cross-validation and other alternative out-of-sample measures are increasingly being used in model selection, and model combination methods are becoming a common alternative to using a single mortality model. Such methods combine predictions from multiple mortality models to reduce model choice uncertainty and improve forecast accuracy. Explore techniques to select and combine mortality models using the freely available open source R package CoMoMo (https://github.com/kessysalvatory/CoMoMo), which extends the widely used R package StMoMo (https://CRAN.R-project.org/package=StMoMo). We will use data for the USA and other countries to discuss and demonstrate four different model combination approaches including simple model averaging, Bayesian model averaging, model confidence set, and stacked regression ensembles.

Description: "Living to 100 in the time of COVID-19: A Study of Late –Life Mortality Trajectories, Leonid A. Gavrilov, Ph.D., Natalia S. Gavrilova, Ph.D., Academic Research Centers, NORC at the University of Chicago Swedish Mortality Does Not Decelerate in Recent Cohorts, or Does it? – Roland Rau, Ph. D., Professor, University of Rostock & Max Planck Institute for Demographic Research, Rostock, Germany; Marcus Ebeling, Ph.D., Karin Modig, Ph.D., Associate Professor, Anders Ahlbom, Ph.D., Professor Emeritus, Karolinska Institute, Stockholm Sweden; Kai Kaufhold, Aktuar DAV, Ad Res Advanced Reinsurance Services GmbH, Cologne, Germany; Annika Ziegler, Lukas Stake, Hochschule Koblenz & Ad Res, Germany"

Description: With the mandated health plan transparency files, health plans and providers are now able to substantially enhance their provider contracting efforts. Although the files are large and cumbersome they are market disruptors and provide new insights for provider contracting efforts. Explore a demonstration on the type of information now available and how it can be used to enhance this from both a health plan and health care provider perspective.