Description: This is a study of Pension Benefit Guaranty Corporation (PBGC) mortality experience. The study consists of data furnished by the PBGC for the time period of September 30, 2009 through September 30, 2014. All of the data come from single-employer pension plans taken over by the PBGC in a distress termination. This is the SOA’s first study of PBGC experience and provides another comparison point of retirement plans mortality experience for work performed by pension actuaries.

Description: The Society of Actuaries' Group Annuity Experience Committee has completed this experience update, which presents the 2015 through 2016 calendar year experience of retired individuals in the United States who are covered under group pension contracts. This update compares these most recent two years of available experience to the 2011-14 period and analyzes how the recent data affects aggregate annual mortality improvement in the group annuity experience data since 2007.

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: This project was developed by the Predictive Analytics Subgroup of the Society of Actuaries (SOA) Research Institute’s Individual Life Experience Committee (ILEC).

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.

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: 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 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: The session will highlight trends in recent industry mortality and lapse studies that have been conducted, including areas where individual companies typically have limited data. The sources of data feature SOA studies as well as other industry studies conducted by consultants and/or reinsurers. Gain an understanding of current drivers of mortality and lapse experience for life insurance evidenced in industry studies, and an understanding of available data to supplement experience in areas where you may have limited internal experience.