Description: Session 4A: Downwind of the Unwind - Medicaid PHE Unwind Impacts on Acuity and Other Actuarial Challenges

Description: Session 1G: Rethinking Catastrophic Claims - Strategies for Sustainable Stop-Loss Pricing

Description: Session 1C: Group Life Experience Update

Description: Future-Proofing Retirement: Technology, Longevity, and Actuarial Solutions

Description: Panelists will discuss ethical dilemmas for retirement actuaries in their day-to-day practices. This session will analyze whether ethical issues have arisen, how to avoid ethical dilemmas, and how to resolve the dilemmas if you find yourself in them. This session will also identify differences between the ethics rules of the Joint Board for the Enrollment of Actuaries and the Code of Conduct for members of the SOA and AAA. Audience members will be invited to participate in the discussion via chat.

Description: The goal of the webcast is to demonstrate through examples how actuaries working in traditional and nontraditional actuarial roles should apply ASOPs when navigating through situational challenges when incorporating AI into their work. This includes collaborating with other professionals, such as regulators, accounting, management, marketing, underwriting, as well as when providing advisory services to external clients.

Description: This webinar will cover the basics of structured assets, their uses in the insurance industry, and ALM and capital considerations. It will also include a section with an introduction to CLO Residual Tranches and recent capital discussions surrounding them.

Description: Intro to The Actuary in the Model

Description: Bias can arise at multiple stages of actuarial data-driven decision making, from data collection and modeling to interpretation and implementation. In this webinar, we consider how different types of bias, such as measurement bias, sampling bias, algorithmic bias, and historical bias, may affect actuarial applications. These biases can distort statistical estimates, undermine predictive performance, or lead to unfair and discriminatory outcomes in insurance pricing, reserving, and risk management. While some forms of statistical bias may be intentionally introduced to improve generalization (e.g., through the bias–variance trade-off), fairness-related biases are more problematic, as they can erode trust, compliance, and social responsibility. This session will leverage academic research to explore real-world actuarial contexts where bias may emerge and to discuss strategies for identifying and mitigating undesirable bias to support more accurate, transparent, and equitable decision-making.

Description: Navigating AI Bias intro