Description: In the third quarter of 2022, publicly traded life insurers reported on their financial and operational activities, which included a downturn in income from alternative investments, the continuing impact of COVID, the impact of equity market returns on variable annuity products, and growth trends related to pension risk transfer transactions.

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: Insurers have long relied on scenario analysis to understand the long-term impacts of potential business decisions, but the advent of rapidly changing market environments and a growing desire to optimize capital has spurred business leaders to leverage new tools in order to explore potential options. Companies are beginning to explore new analytical solutions that trade-off accuracy for speed for a more robust approach to impact quantification. In particular, they are looking at the use of additional what-if scenario models, built from the top down, to support decision-making processes. Actuaries can help take charge in the development of these top-down analyses by leveraging data and actuarial techniques to supplement their existing models. In this way, business leaders can simultaneously rely on the actuarial rigor of typical standalone cash flow projection models, while also receiving support for real-time business decision-making. The potential of what-if scenario modeling can help actuaries unlock a powerful new approach to scenario analysis to enable more rapid decision-making. Learning objectives: In this session we will discuss the fundamental concepts of what-if scenario modeling in an actuarial context, including: - Core principles and components of what-if scenario modeling - Common challenges and considerations around the implementation of what-if scenario modeling - Potential solutions to challenges and their implications

Description: According to Oliver Wyman's 2023 Asset-Liability Management (ALM) survey, long-term care (LTC), universal life, and whole life insurance products have among the longest liability duration profiles, often with material asset-liability duration mismatches. Deploying an optimal strategic asset allocation (SAA) framework for these products is particularly challenging due to their: - Long-tail profile. Liability cashflows for long-duration insurance products often extend beyond the investment horizon of currently available assets (e.g., 30-year U.S. Treasuries) - Liquidity needs. Depending on the type and age of the insurance product, achieving your desired levels of projected liquidity becomes instrumental Solution: A robust liability-driven SAA framework with an emphasis on minimizing cashflow mismatch while considering a company's risk appetite and investment guidelines aims to reduce interest rate and liquidity risks. Case studies: LTC and life-focused case studies will provide insights into hypothetical SAA frameworks, including ways companies optimize their fixed-income assets. We will also explore techniques used to extend asset duration beyond a typical fixed-income portfolio, using derivatives and alternative assets.

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 paper applies the research in Prospect Theory of Kahneman and Tversky to explain why sales of annuities lag sales of life insurance products. The author theorizes that life insurance is easier to sell because customers perceive themselves to be in the domain of losses, while potential annuity customers perceive themselves to be in the domain of gains. Kahneman and Tversky showed us that people are mostly risk averse in the domain of gains and risk seeking in the domain of losses. Purchase of an annuity or life product is a risky investment. Customers will find it easier to take the risk in the life insurance domain of losses than the annuity domain of gains. The author then proposes a hybrid life annuity product that might address both mortality and longevity risk in one product that would allow both products to be more competitive than stand-alone products.

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

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: 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.