Description: This request for proposals for research studies on measuring and modeling climate liability risks outlines the background, objectives, proposal requirements, selection process, submission details, conditions, and other pertinent information for interested researchers.

Description: This report discusses the challenges faced by the insurance industry due to climate change, focusing on availability, affordability, and adequacy of insurance in areas impacted by climate-related risks. It covers expert panel discussions, factors influencing insurers, market gaps, solutions, and funding sources.

Description: As our industry rapidly adopts more advanced technologies, today's actuaries have more data and greater analytical capabilities than ever before. Nevertheless, these advancements come with their own set of challenges.Come explore the tools and techniques to optimize your processes and avoid common pitfalls so that you can get the most out of your experience studies.

Description: This webcast will explore hedge strategies that can take advantage of synergies with existing business, improve liquidity and collateral management, incorporate opportunistic design-making, and reflect a company’s appetite for certain risks where excess returns can be earned.

Description: The article summarizes the SOA survey conducted of risk professionals regarding their understanding of important risk terms. The results contain perceptions by individuals and also firm-level information. Survey responses clearly demonstrated high differences in risk definitions.

Description: This session will highlight the growing significance of climate risk for insurance companies, emphasizing how to prepare for regulatory requirements as well as the impacts on financial stability and operational resilience within the industry. Attendees will gain insights specifically addressing climate risk within insurance operations including enhancing governance structures, ensuring regulatory compliance, identifying and monitoring metrics and key risk indicators, and conducting scenario analyses. Through real-world case studies and perspectives from both life and P&C practitioners, participants will leave equipped with practical strategies to navigate the complexities of climate risk management, foster transparency and accountability, and effectively address the urgent challenges posed by climate risk within their organizations. Learning Objectives: - Understand the evolving landscape of climate risk and regulatory requirements for climate risk disclosure. - Gain insights into effective approaches and current best practices that actuaries and risk modelers may use in performing qualitative and quantitative climate risk scenario analyses. - Learn practical strategies for integrating climate risk considerations into insurance operations.

Description: The mismatch between how interest rate impacts the value of assets and liabilities could have material negative impact to companies’ earnings and surplus. To quantify and mitigate the impact of such mismatches, companies have used matching a combination of duration and convexity. I will review these measurement techniques and their limitations.

Description: Global trends in both technology and regulation are causing US insurers to reflect deeper on their model risk management frameworks. The world has seen a rapid proliferation of AI-based tools accompanied with an overall increased usage of machine learning models, exacerbating model risk and reducing transparency in an unprecedented manner. This rising tide of AI means that it is imperative for US-based insurers to address these emerging risks within their Model Risk Management (MRM) frameworks. Regulation also continues to evolve in this space: in Canada, the Office of the Superintendent of Financial Institutions (OSFI) revised Guideline E-23 to instill more detailed model risk management standards; in Bermuda, the Bermuda Monetary Authority (BMA) has published CP2 to add increased scrutiny on the data and models used by insurers. Insurers must react: MRM frameworks require prudent revisions to reflect these ever-changing landscapes of risk. Reassessing the core pillars of MRM frameworks - risk measurement, model development, validation, monitoring, and governance – is key to understanding the consequences of these evolving market changes on insurers’ risk profile. Overall, the inclusion of these new tools into the world of insurance necessitates a thorough examination of insurers’ MRM frameworks in order to align these new risks with core MRM principles. In this session we will: - Reflect on recent drivers of change and how they should be considered in the context of MRM - Explore best practices as it relates to MRM frameworks in the context of current global trends - Describe challenges, success stories, and lessons learned relating to recalibrating and embedding MRM frameworks

Description: Amid the growing adoption of AI/ML models for underwriting and pricing, Life and Annuity carriers are facing increasing regulation in North America on Model Risk Management (MRM) and the use of AI. Colorado has set a precedent in the US by requiring reporting in June 2024, with other states looking to follow suit. To ensure compliance, Chief Risk Officers and Heads of MRM, working with their business partners, will need to evolve their MRM and AI risk frameworks, enhance model validation capabilities, and strengthen governance. This session will present: - Key trends and challenges faced by carriers amid increasing model landscape complexity and regulatory expectations - Regulatory outlook and implications for carriers' MRM and AI risk programs - An approach to enhance MRM and AI risk frameworks, including specific model validation program updates to address the unique risks of AI/ML models - Opportunities to enhance both MRM validation effectiveness and efficiency through the adoption of more risk-based approaches, smart sourcing, and automation - Case studies of how leading carriers are evolving their MRM and AI risk programs Upon attending the session, the attendees will develop a good understanding of increasing regulatory expectations, the required MRM and AI risk framework enhancements, and approaches to design and execute an effective MRM validation program in a cost-efficient manner.