Description: "Focuses on innovations in financial reporting processes that connect actuarial results with finance in a clear, efficient way. The session covers tools and techniques for automating data flows from actuarial models to financial statements, reducing manual work at quarter-end. It also highlights the use of visualization and business intelligence (BI) tools to communicate valuation results (e.g., interactive dashboards for reserve roll-forwards or attribution analysis). By sharing success stories of automating FP&A workflows and adopting self-service analytics, presenters will show how actuaries can provide more timely insights to management. Attendees will take away ideas for improving reporting accuracy, audit trail, and the strategic value of actuarial information in decision-making."

Description: This document is a call for essays issued by the Society of Actuaries Research Institute. It seeks submissions that explore the impact of Artificial Intelligence/Large Language Models on retirement professionals and retirees. The document provides background information, research objectives, and a list of illustrative topics and questions that the essays can address. The submission guidelines, timeline, and evaluation criteria are also outlined.

Description: In this session a panel of industry experts from the life insurance industry will examine the modeling challenges created by increased investment in more esoteric assets.

Description: CMS has required hospitals to publish their chargemaster rates, cash discounts and negotiated rates as of January 1, 2021. Our panel is comprised of a data strategist, an executive from a large hospital system and an actuary.

Description: We explore four plausible scenarios for how AI might reshape the future, focusing on employment as the main driver. At one extreme, AI boosts productivity, sparks entrepreneurship, and leads to near-full employment through human-AI collaboration and reformed education. At the other end of the spectrum AI-induced mass layoffs trigger an economic spiral, with soaring unemployment, creative stagnation, and societal resentment. The middle path presents a fragmented transition with a two-tier workforce, periodic recessions, and variable adaptation success. Lastly AI fizzles due to poor returns and rising costs, causing economic stagnation and investor losses without widespread disruption. Each scenario also includes potential impacts on the insurance industry, ranging from innovation and growth to regulatory strain and investment losses in the others. Proactive scenario planning, by preparing for multiple futures by identifying early warning signals, adapting plans, and strengthening institutional resilience is encouraged. The article closes with four additional speculative scenarios, including government-controlled AI, AI dominance, militarized AI, and mass surveillance. Ultimately, the piece argues that while AI’s future is uncertain, the choices made today will shape whether it becomes a partner, a threat, or a disappointment.

Description: This report explores how artificial intelligence can be applied to investment and retirement planning, highlighting real-world applications, risks, and recommendations.

Description: Artificial intelligence is rapidly transforming the investment landscape. From natural language processing and large language models to physical AI applications like robotics, autonomous vehicles, and drones, the evolution of AI is reshaping both how we analyze data and where we find opportunity. We’ll begin by exploring the evolution of AI, which is already driving disruption across industries such as coding, customer service, and marketing. However, the future of AI is physical AI (drones, AVs, robotics). With valuations already stretched, and growing concerns about the “circular economy” and bubble risks, investors should focus on high-quality tech companies that generate free cash flow and deliver strong returns on invested capital. We'll explore examples of interesting companies across the globe: Meta, Alphabet, and Uber in the US; ASML in Europe; Tokyo Electron in Japan; TSMC in Taiwan; and BYD in China. The session will also examine how natural language processing and large language models are reshaping the way quantitative and fundamental managers extract insights from raw data. We’ll highlight how these tools are being deployed to enhance understanding of risk and return, and how they’re increasingly supporting collaboration between quant and fundamental teams.

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.

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: Financial planning and analysis (FP&A) has become increasingly important to the life insurance industry in recent years. Insurance companies must navigate complex changes in financial reporting and capital requirements to manage risk effectively. Furthermore, technological growth has changed the landscape of the theoretical capabilities of FP&A; however, many FP&A areas struggle to implement the sophisticated modeling techniques required to manage the long-term liabilities associated with life and annuity products.