Description: This paper discusses a theoretical framework for Enterprise Risk Management, risk dynamics within property-casualty insurance companies, empirical findings on widely used risk models, and examples of effective risk models.

Description: This paper explores areas where fuzzy logic models may be applied to improve risk assessment and risk decision-making. It discusses the methodology, framework and process of using fuzzy logic systems for risk management. With the help of practical examples, it is hoped that it will encourage wise application of fuzzy logic models to risk modeling.

Description: This research study was designed to answer the following questions: 1. What are the most commonly occurring risk categories? 2. What are the most commonly occurring risk subcategories? 3. What are the most commonly occurring individual risks? 4. Which individual risks tend to make front-page news? 5. Which risks generate the most extensive coverage in the form of repeat articles? The scope was defined as significant downside risk events experienced by Canadian nongovernmental organizations as reported in the first 10 pages of either Section A (Front) or Section B (Report on Business) of The Globe and Mail in a 2012 weekday (Monday-Friday) edition, as recorded in their online archives (“GlobeArchives”).

Description: The presenters describe the process payers and employers can use to evaluate emerging medical device technologies, recent or pending FDA submissions for prescription drugs and experimental treatments that impact patient care, resource utilization and health care expenditures. Learn the process of identifying and selecting impactful treatments (Horizon Scanning) and quantifying the financial cost of new technologies before they arrive in the market. These workflows can be leveraged by actuaries, healthcare economists, clinicians and pharmacists from a payer (Commercial and Medicare) and employer perspective for self-funded plans. Walk through sample models that illustrate how costs for emerging technologies and treatments can be modeled.

Description: IFRS 17 is causing insurers to rethink their investment management and ALM strategy. The presenters examine issues arising under IFRS 17 and IFRS 9 and the resulting challenges facing insurers, including the volatility and impact on financial statements. Solutions will be proposed including a discussion on how to deal with the disconnect between economic and accounting results. A case study will present the tradeoffs insurers must consider to optimize their portfolio under IFRS 17.

Description: The Society of Actuaries announces the release of a new report that will provide a resource for the insurance industry to learn about image recognition technology. Authored by Kailan Shang, the report explores the current status of image recognition technology, models for image recognition, and various possible applications of image recognition to insurance.

Description: This report examines the use of a copula approach to build a predictive model to estimate the amount of economic capital a life insurance company needs to protect itself against an adverse movement in interest rates, mortality, and other risk drivers.

Description: This is report summarizes the results of a Joint Risk Management Section sponsored study on negative interest rates and the insurance industry.

Description: The Casualty Actuarial Society, Canadian Institute of Actuaries, and the Society of Actuaries' Joint Risk Management Section and the Joint Risk Management Research Committee are pleased to make available a new report on the application of ERM. Authored by Bonnie-Jeanne MacDonald, Sim Segal, Michael Wolfson and Heidi Walsh, the report provides a novel demonstration on how ERM can be applied to serve the Canadian public by informing decision makers on the risks facing the current and future delivery of long-term care.

Description: The SOA’s Artificial Intelligence Working Group explored the role of AI in actuarial practice, focusing on risk management, regulatory compliance, and ethical considerations. The discussion highlighted the importance of the NIST AI Risk Management Framework and Generative AI Profile in ensuring responsible AI development and deployment in insurance and healthcare.