Description: Feature article discussing key accounting concepts in finance.

Description: In this session, we will explore the innovative use of artificial intelligence in developing narrative scenarios to enhance life insurance investment strategy discussions. Participants will discover how developing comprehensive and imaginative scenarios can help identify potential future risks and opportunities, leading to more informed investment strategy planning. The session will cover practical approaches to creating and utilizing narrative scenarios, emphasizing their role in improving decision-making processes. Join us to learn how to craft compelling scenarios that provide a broader understanding of drivers of emerging trends and uncertainties, ultimately transforming traditional investment strategies in the life insurance sector. Learning Outcomes: - Understanding the role of AI in Scenario Development - Enhancing Investment Strategy Decision-Making - Writing comprehensive scenarios and using them for specific investment strategy decisions

Description: Speakers representing investment and risk management teams share how they are integrating climate risks into their investment management practices. We will discuss methods to measure and assess climate risks in the investment portfolio, for both transition risks and physical risks. Speakers will also touch on how climate risks can be integrated into the bottom-up investment process. Lastly, we discuss climate-action-oriented programs to decarbonize investment portfolios and their challenges. At the end of this session, participants will be able to: - Understand methods and challenges to measuring exposure to climate risks in their portfolio - Understand methods and challenges in integrating climate risks into the investment process - Understand methods and challenges in implementing decarbonization programs such as Net Zero.

Description: This article is dedicated to the non-statutory accounting treatment of common credit risk exposures of life insurers but does not cover all aspects of non-statutory accounting standards. It introduces the concept of and the valuation of credit risks, concerning life insurers.

Description: While the major market crash subsequent to the widespread contagion of the COVID-19 virus was readily predictable, the rapid recovery and ongoing growth subsequent to it was not. Market dynamics around the recovery resist characterization by most traditional analytical approaches. In examining the circumstances around the emergence of a large retail class of investors, one sees that ingrained human tendencies, particularly optimism bias, have fueled much investor behavior responsible for the recovery. Understanding this makes it possible to predict potential future crashes and develop behavioral interventions for subsequent market recoveries.

Description: Introduce basic concepts of the Health Risk-Based Capital and show case a few strategies around Risk-Based Capital.

Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).

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: This session will discuss basic and advanced hedge modeling concepts. It will explain common hedge strategies used to support guarantees and options embedded within life and annuity products and the materiality of these products within the US. Attendees will learn how Machine Learning acts to reduce the computer processing needed to simulate the dynamic hedging of product liabilities projected in the future across many stochastic scenarios. Actuaries working in consulting, software development, and a large life insurance company will provide a wide range of hedging viewpoints ranging from a broad industry perspective to a hedging practitioner. Attendees will obtain a comprehensive understanding of a novel ML approach to modeling dynamic hedges and the hedge optimization opportunity it presents.