Description: This report summarizes industry mortality experience for the period 2009 - 2016. This report is updated on a mostly annual basis and based on information aggregated from industry VM-51 submissions

Description: Join a discussion on the evolution of asset and liability projection processes in the life insurance industry. Familiarize yourself with the broad set of inputs, stakeholders, and financial planning and analysis use cases. Notable areas of focus will include, but are not limited to, historical background, modeling needs, interactions between stakeholders, industry pain points, and a composite of an ideal future state informed by perspectives gleaned from leading companies within the industry.

Description: The life insurance industry has a lot going on. Come discover some key highlights of the life insurance market mixing quick hits with some in-depth conversations about the headlines and hard lines happening in the life insurance sector. Topics include underwriting, regulation, product changes and distribution to name a few. If you enjoy the PTI show for sports, you will enjoy hearing about the life insurance industry in this format and hosts will run down the headlines of the insurance industry in PTI fashion.

Description: Situated at the intersection of psychology, neuroscience, sociology, and economics, behavioral economics (BE) centers on applying an honest understanding of human behavior to practical problems. This report is designed to provide general familiarity to the reader interested in the subject of BE and motivate additional research on the topic.

Description: Supplemental Health, DI & LTC Conference

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: In this session, presenters will share the impacts of transition to IFRS 17 on equity and various KPIs across insurance companies (Life & P&C), as well as share some observations of what the IFRS 17 landscape looks like across different global regions. The session will conclude with lessons learned and expectations for emerging practices under IFRS 17 globally.

Description: Report summarizing the results of the 2018 PBITT UL with Secondary Guarantees Survey

Description: A literature review was performed on mortality improvement, socioeconomic differences in mortality and their links to specific causes of death, and methods for analysis. It highlighted the fact that socioeconomic status (e.g., income and education) is closely linked to differences in mortality and that the longevity gap between socioeconomic groups is growing over time. It also informed the selection of key variables and the need to view mortality improvement as a complex interaction between specific causes of death.