Description: Model design is an important part of being an actuary, and the SOA highlights the importance of the actuarial control cycle of model design, evaluation and monitoring. What tools are at our disposal for these steps? When should we be concerned or excited about model performance? Can we, and should we, use machine learning tools to assist with model improvement or development?

Description: The American Academy of Actuaries Health Solvency Subcommittee and the NAIC Health Risk-Based Capital (E) Working Group are in the process of analyzing and reviewing the H2-Underwriting Risk component and the managed care credit calculation in the health risk-based capital (HRBC) formula. Come learn what changes are ahead! While RBC is designed for solvency issues, also come hear about other ways health insurers manage their capital and surplus levels. Are others doing something you should try?

Description: New web page that describes the new e-learning FSA module Scenario Modeling. It replaces Financial Reporting Module in the QFI track.

Description: This training session will include lessons learned in 2021 and revisit a presentation given last year, discussing the new Actuarial Standard of Practice (ASOP56), which fills in the gap for guidance on Actuarial Modeling. Prior to ASOP 56, actuarial judgement reigned where standards and guidance lacked. Learn the foundation for designing, developing, modifying and validating a model according to SOA guidelines. By attending this session, you will: • Learn how to identify model’s intended purpose. • Strategically extract input data and assumptions. • Use the level of detail required in output data as a consideration for model design. • Choose from SOA recommended validation techniques.

Description: Come dive into the Introduction to Modeling Summer Boot Camp series, which will provide an overview of modeling topics and techniques aimed at the beginner modeler. This interactive Zoom series will be comprised of five 90-minute sessions (August 24, 25, 30, 31 and September 1), with opportunities to discuss the topics in small break-out groups.

Description: The Student Research Case Study Challenge provides an opportunity for teams of students to apply their actuarial skills on a real-world problem in a business setting.

Description: In this session, we will do a deep dive into the potential of large language models; we will explore how new AI tools can be combined with traditional modeling to automate aspects of risk assessment. Industry experts focused on operationalizing this technology will talk through their approaches, lessons learned and demo use cases. We will also share insights around creating a robust AI strategy based on business objectives and regulatory considerations. Learning Outcomes: • Applications of large language models in risk assessment use cases • Regulatory considerations around the use of generative AI

Description: A disscussion of actuar R, an R package for actuarial science, and its facilities in the fields of loss distribution modeling, risk theory and credibility theory.

Description: This is the second of a two-part article introducing and elaborating an “Adaptor” strategy for ERM. Such a strategy is intended to fulfil two purposes: first, switching among four styles of risk-coping strategies in order to track qualitative changes in a business’s risk environment; and, second, to support the business in navigating with resilience through all the seasons of a business-economic cycle, i.e., of boom, bust, moderate, uncertain. This latter requires careful study of the nature of resilience, the familiar idea of bounce back, and the less familiar idea of bounce forward. Part I of our article is underpinned therefore by a supplementary document (available from https://riskviews.wordpress.com/adaptor-exceptionalism/), which addresses the issue of structural change in a business or economy. It draws heavily on ideas taken from the discipline of control engineering, as well as some from ecology, social anthropology, and economics. The entire two-part article is thus an exercise in cross-disciplinary Systems Thinking.

Description: Regression to the mean is a phenomenon that arises when comparing sequential data points. It states that when an initial observation is extreme or an outlier, future observations will be closer to the average. Health care actuaries need to understand and be able to mitigate the effects of regression to the mean to avoid misapplying predictive models or misstating measurements of historical effects.