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: The Society of Actuaries will be offering this webinar series intended to educate and investigate aspects of life insurance, annuity, and long-term care product tax compliance. Developed by the Taxation Section, participants will be brought up to date on recent Internal Revenue Service (IRS) guidance and learn about the newest tax issues related to product design and administration. Legal, IRS and product development experts will provide vital insights into the challenges of developing and administering products within the requirements of the Internal Revenue Code (IRC) and allow ample opportunities for questions and information exchange. This session will provide an intense introduction to the basics of product taxation, with a focus on life insurance and the requirements of Section 7702A.

Description: Defining fairness for life insurance processes is complex and challenging as well as subjective. This webcast will be a discussion of the challenges and opportunities presented as our profession rethinks fairness in the context of processes involved in developing and managing life insurance, especially risk classification.

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: 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: Gain a high-level understanding on how long-duration targeted improvement (LDTI) impacts insurance products and earnings. Explore highlights on secondary impacts to companies due to additional complexity of LDTI, such as a need for a source of earnings analysis. Then dive into the details and hear an explanation on how earnings emergence differs for each type of life and annuity product impacted by LDTI. Finally, discuss the impact -to-earnings volatility under LDTI for each product type. Close the session by connecting how the investor story changes with all the impacts discussed during the session.You should walk away with a good understanding of LDTI earnings emergence, which is a topic that has very limited thought leadership available today.

Description: Discover a modernization journey within finance, actuarial investment and risk domains. Expect to learn about the world we are striving for - an event-based, self-service, automated environment that centers around a cloud-based data lake with proper data quality and governance and provides visual, on-demand solutions for reporting and analysis. We will educate the audience on the technology used to obtain this vision and the approach (including our team structures and project management ideologies) we have taken thus far to start moving there.

Description: Behavioral economics research has shown that small details can have a large impact on how a person behaves. As life insurance continues its evolution into the digital domain, life insurers now have an almost infinite number of ways to tweak their product offering, positioning, and delivery to the end consumer. Behavioral science is becoming increasingly more important to optimize these product design decisions and improve outcomes for both insurers and their customers. Discover a brief overview of key behavioral economic concepts and dive into two SOA research projects that are exploring how behavioral economic techniques can be used in different parts of the life insurance value chain. First, you'll start before a policy is even sold, by exploring how behavioral economics, through techniques such as the messenger effect, can be used to increase consumers' comprehension of policies and life insurance's value. Second, you'll look at how underwriting question design can impact applicant behavior and lead to higher quality application data. At the conclusion, you'll enjoy having a better understanding of how behavioral economic techniques can be applied in a life insurance context.