Description: The NAIC continues to move ahead with the GOES (generator of economic scenarios) project for the transition to a new real-world economic scenario generator (ESG) prescribed for principle-based reserve and capital calculations. This session will provide a detailed project update from the regulator and practitioner perspective. An overview of model details and methods that have been decided, what remains to be determined, and the projected timeline for implementation will be discussed. Recent developments such as the industry field test and NAIC model office testing to assess the potential impact on reserves and capital will be discussed as will a review of the implementation of the stochastic exclusion ratio test (SERT). We will also discuss the components of the ESG model and the associated scenario sets the NAIC has exposed with respect to target and acceptance criteria. At the conclusion of this session, attendees will understand the current status of the NAIC GOES project and its projected timeline and will be able to describe the components of the model and how the model has been calibrated and validated. Attendees will also appreciate the remaining challenges in bringing the model to full implementation.

Description: Behavioral Science is a powerful tool to enhance customer experience and improve outcomes across the life insurance value chain. This session will cover the foundations of behavioral concepts in a life insurance context and discuss practical areas that can benefit from behavioral science including customer engagement, disclosure, and retention. The session will leverage recent SOA research and specific experience in communication campaigns and call center best practices to translate theory to quantified results. Attendees will leave with an appreciation for potential challenges and practical steps to measure and demonstrate the value of implementing behavioral science.

Description: This session on Innovations in Underwriting will explore the latest advancements in underwriting technology and methodologies. It will focus on how these innovations are transforming the insurance industry, making underwriting processes more efficient, accurate, and customer-friendly. Topics will include the integration of artificial intelligence (AI), machine learning, and big data analytics into underwriting workflows. Additionally, we'll discuss emerging trends in risk assessment, automation of underwriting tasks, and the implications of these changes for underwriters and insurance professionals. Attendees will have the opportunity to learn from industry experts and participate in discussions on how these innovations are reshaping traditional underwriting practices. Learning Outcomes: - Gain an understanding of the latest technological advancements impacting underwriting, including AI, machine learning, and data analytics. - Learn how these innovations can streamline underwriting and pricing processes, improve accuracy, and reduce costs. - Explore new risk assessment methods and discover how automation is changing the underwriter's role. - Identify best practices for integrating innovative technologies into existing underwriting and actuarial pricing frameworks.

Description: Artificial intelligence (AI) presents numerous opportunities in the insurance and financial industries. It also comes with heightened model, operational, legal and compliance, financial, and data risk. In this session, we will discuss speculation that emerging AI capabilities have sparked the fourth industrial revolution, examine what is different about this most recent round of capabilities, and examine why industry experts warn about the heightened risks the use of AI presents. We will review a framework - which includes AI ethical principles, standards, and best practices - to govern the use of AI. This will include a cross-reference of relevant Actuarial Standards of Practice as well as industry guidance like the NAIC Model Bulletin on the Use of AI Systems by Insurers. This framework will help actuaries and insurance professionals gain confidence that - if governed appropriately - we can reap the vast rewards of using AI systems in our industry.

Description: Discover investing trends across the life and health industry as insurers seek to navigate rapidly changing rate and market environments. BlackRock will present results from its Peer Risk Study, which models industry holdings based on statutory filings at the CUSIP level. Through the Aladdin risk modeling software, expect a production of deep insights into insurer portfolio risk and return characteristics, as well as exploring how they have evolved. Keep an eye out for a partner to present on the evolution from their perspective. Start learning about what is happening on the asset side of the balance sheet across the industry.

Description: In this talk, we look at two behavioral aspects: from end customers perspective and through the lens of underwriters. We will examine simple rules to improve the insurance underwriting decision to judge whether a risk is insurable and whether they have impact on increasing underwriting decision quality.

Description: Come discuss modeling costs and its governance, as well as introduce some platforms for more granular asset modeling.

Description: Analyze the market volatility behavior in context with historical extreme events during this webcast. There will be a focus on understanding volatility clustering and relationships, both contemporary and temporal, in addition to key economic and capital market variables. You'll also learn from a comprehensive attribution analysis, which uses economic data, asset data, retail investor data and event data as explanatory variables. Dive into investment-related Reddit comments used to capture features of retail investors and tree-based models, including Random Forests and Gradient Boosting Machines to justify changes in volatility modeling. Expect this webcast to end with an example of liability-driven investment optimization that illustrates the potential impact of increased volatility assumption, how to adopt stochastic volatility models, how to reflect volatility clustering and return jumps.

Description: This session will consider how catastrophic gaps in risk management practices contributed to the failure of regional banks in the first half of 2023. Panelists will explore ERM practices and potential corrective actions, based on insurance industry and pension plan best practices, that could have mitigated against the failures.

Description: Deciding where to spend that next dollar of capital is at the heart of financial management. Executive teams regularly grapple with decisions on whether to purchase a new Enterprise Resource Planning (ERP) system, invest in growing a distribution channel, expand into a new geographic area, or develop a new product, among many. The great news is there is no shortage of sound financial theory and logical rationale for making these transformational decisions. Yet business literature is filled with examples of decisions that did not meet expectations, sometimes materially missing the mark when comparing experience to assumptions. This is particularly the case with mergers and acquisitions, where multiple studies have shown that a sizable majority of such transactions do not achieve their intended outcomes. While some of the disappointing returns are attributable to random chance and unfortunate circumstances, a material number of them can be traced back to cognitive issues that arose during the decision-making process. Mental shortcuts or heuristics taken by individual decision-makers in evaluating information, as well as organizational dynamics such as groupthink both contribute to sub-optimal choices involving significant outlays of money. Being aware of these and having established protocols to mitigate against them can substantially improve decision-making around capital allocation and reduce misallocation of funds. In this session, the panelists discuss several common cognitive biases that can adversely impact the capital allocation decision process, introducing the notions of time, accounting convention, positivity, hierarchy, and social proof in describing them. Historical case examples of sub-optimal capital allocation are then explored, identifying the contributing behavioral factors including the simultaneous confluence of multiple factors. The panelists will close the session with several suggestions for attendees to consider for incorporation into their decision-making processes. At the conclusion of the session, attendees will be able to: - Learn common cognitive biases that impact capital allocation decisions - Differentiate which biases impact different aspects of the capital allocation process - Build awareness of biases affecting capital allocation decision models - Mitigate cognitive biases present in decision making processes