Description: Recording

Description: Join us for this popular annual webcast covering the latest considerations for the year's U.S. statutory financial reporting work. Always informative and occasionally entertaining, this webcast offers a chance to hear from three expert speakers, who will discuss asset adequacy testing and this year's New York and California holiday letters, the impact of recent interest rate changes, potential changes to statutory financial reporting due to climate change, and recent proposal(s) impacting asset adequacy testing for reinsured business.

Description: Inflation can impact retirement and retirees in a variety of ways. With inflation as a reality in the current economic environment, the Society of Actuaries Research Institute's Aging and Retirement Strategic Research Program has explored this issue in recently published research and essays. This webcast will present results of that work, sharing different aspects of inflation's impact on retirement including savings, investment choices and the impact on retirees themselves.

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.

Description: Among the many impacts of the Inflation Reduction Act’s redesign of Medicare Part D will be a change in Part D cash flows. Plan sponsors have always had to balance the seasonality of prescription drug costs against reimbursement at various times from monthly subsidies, risk score true-up payments, rebates, manufacturer contributions, and CMS settlements, but the IRA is changing the pattern of cash flows in 2024, 2025, and beyond. Moreover, the risk-adjusted portion of CMS revenue is likely to grow in 2025, even as the Part D risk score model changes.

Description: Generative AI has become a hot topic since ChatGPT became one of the most well-known Machine Learning models ever. There are many different generative models out there, and in this presentation, we explore the possibilities, benefits, reservations, and alternative use-cases of using Generative Adversarial Networks to generate health insurance claims data.

Description: Listen to pre-eminent healthcare actuaries present their pitch for how they'd fix US healthcare (whatever the pitch thinks 'fix' means) if they were supreme policy maker for a day. No idea is too big, and no healthcare actuary's dream is too small.

Description: The session will provide attendees with a framework of what goals to think about when building and utilizing a predictive modeling framework in New Sales and Renewal Underwriting mid-market, level funding, balance funding, and self-funding group health business.

Description: "Spread pricing is a pricing methodology where pharmacy benefit managers (PBMs) bill a payer one price but reimburses the dispensing pharmacy a lower price for the same prescription drug. PBMs have historically relied on spread pricing as a revenue source. However, a growing number of states are introducing and passing legislation prohibiting spread pricing for Medicaid and commercial clients. In addition, new entrants to the PBM and mail-order pharmacy space are disrupting the market by offering services that promote increased transparency and member satisfaction. Expect updates on recent legislative trends and market disruptions targeted towards increasing PBM drug pricing transparency. By attending this session, you will have a better understanding of how changing legislation and market disruptions may impact current pricing methodologies."

Description: "Explore a general introduction to the opportunity of applying a relatively newer area in machine learning called uplift modeling, which has been successfully used in retail (Wayfair etc). The concepts are parallel to the intervention programs in health care, where we are interested in measuring the treatment effects and ultimately directing the right members to the most effective programs, driving the highest value for the programs both from a financial and health outcomes perspective. Uplift modeling can also be viewed in context of causal modeling where we are interested in assigning cause to a treatment relative to a member not receiving the treatment. Check out an introduction on the concept, seehow it relates and builds on existing work, and use a demo to showcase the potential of integrating this ML approach to current work."