Description: This article is historical in nature and explores the impacts of traditional life insurance products offering cash surrender values. This article intended to show that by offering cash surrender value, insurance companies became less exposed to the risk of mis-estimated lapse assumptions. This article presents an illustrative case study to highlight this dynamic. This article also explores the issues with traditional life insurance products that arose in the 1970s and 1980 and led to the introduction of Universal Life in the U.S.

Description: Current tax issues affecting the insurance industry, including the Inflation Reduction Act (Budget Reconciliation Bill)'s tax provisions, including the corporate minimum book income tax and share repurchase (or stock buyback) excise tax; the tax treatment of non coordinated benefits (also known as fixed indemnity benefits); and an update on the OECD’s Base Erosion and Profit Shifting Project

Description: This article summarizes a recent session at an SOA meeting that presented tips on improving and speeding the product filing process.

Description: This article helps companies better understand the currently proposed upcoming VM-22 regulation by comparing the VM-22 measurement model to LDTI MRBs. The article highlights key considerations as companies plan for adoption of these two measurement models. These considerations should help inform a company’s strategy related to the business impacted by these standards.

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: This video from Ben Keslowitz explains the concept of reinsurance, which is a financial arrangement where insurance companies transfer portions of their risk portfolios to other parties, known as reinsurers. This helps insurers manage large or catastrophic claims and optimize their risk components. The video also covers different types of reinsurance, such as proportional liability reinsurance, non-proportional reinsurance, and quota share coinsurance, each serving different purposes in risk management. Additionally, it discusses the reasons insurers might choose to reinsure their business, including liability risk mitigation, investment risk transfer, and entering new markets.

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.

Description: This report models and forecasts future mortality improvements in China, by geographical regions, as well as by causes of death. Deaths and population exposures data provided by the Chinese Center for Disease Control and Prevention was used for the period 2004–2019.

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