Description: Modern actuarial work, being quite data intensive, provides many opportunities for actuaries to use the statistical skills that are part of their basic training. Furthermore, modern computing allows us to analyze vast quantities of data intensively, and each package will provide statistical 'answers' to any questions the actuary might ask of it.

Description: Future mortality improvement is an important assumption in the valuation of life insurance. In this session, presenters will discuss the work of the Life Mortality Improvements Subgroup (LMISG) of the Society of Actuaries (SOA) Mortality and Longevity Oversight Advisory Council (MLOAC) developing mortality improvement scales for individual life insurance.

Description: The session will focus on International Financial Reporting Standards 17 (IFRS17) implementation, including methodologies (classifications, different measurement models, fulfillment cash flows, and contract service margins) for various U.S. products, operational challenges, controls, IFRS17/GAAP comparisons and analytics.

Description: Cover a variety of topics centered around the unsupervised learning technique called 'clustering'. The presenters will take you through a wide array of topics that range from 'What is Clustering?' to 'How can I use this technique to solve problems I didn't even know existed?' In the health care industry, everyone is looking for an edge in reducing total spend-cluster analysis finds the unambiguous in an ambiguous framework of results.

Description: Actuaries historically modeled new launches in pharmaceuticals by anticipating the expected change to the market basket. This became more complicated with the recent launches of gene and cell therapies as they address rare diseases. Rare conditions have low total member volume to study and some do not currently have a diagnosis code to identify them within claims data. Addressing this modeling challenge has been growing more important in the recent years with the number of therapies launching. What has the health care industry, and more specifically health care, done to enhance modeling and how should this be altered for the future? Additionally, historic modeling has sometimes incorporated a level of direct medical treatment cost offsets where studies have shown lower treatment costs are achievable. Can gene and cell financial modeling include cost offsets as well? And if so, what are suggested precautions or limitations to this effort?

Description: Enjoy an in-depth walkthrough of the facets of the lab testing world in this webcast. The agenda will include a discussion on genetic testing, which focuses on its general background, current trends and tests in the pipeline, as well as a focus on the approaches to achieve savings in lab testing. Finally, expect to check out sample actuarial analyses that will be used to identify areas of focus in lab management. Join us and take a deeper look into lab work in the actuarial health industry.

Description: Renal disease is a very heterogenous medical condition whose end state is dialysis, a recurring cost intensive medical condition. Dialysis cost is one of the major medical cost drivers in USA. Insufficient and improper care management in the early stages of CKD (chronic kidney disease) leads to early incidence of end stage renal disease (ESRD) among CKD patients which not only leads to high medical costs, but also reflects poor health quality.

Description: Generally accepted accounting practices (GAAP) reporting frameworks are more involved than ever before. From disclosures, to assumption changes, to interest rate movements, uncovering the drivers of income emergence can prove hard to do, and even trickier to communicate. This session will revisit how the updated standards create layers of complexity and offer ways to side step these obstacles to generate useful earnings analysis by various drivers.

Description: This session will explore governance considerations for insurance carriers leveraging machine learning models. During the session, panelists will share insights on the evolving predictive model and AI regulatory landscape, as well as techniques for developing or enhancing AI governance frameworks that comply with emerging regulatory guidance.

Description: Hot Topics in Setting Pension Return Assumptions is the third in a series, following Mathematics of Pension Return Assumption and Inflation, focusing on Effects, Forecasts, and Role in Pension Valuations. What time horizon is more appropriate for Capital Market Assumption (CMA) forecasts: mid-term or long-term time horizon, or a blend of both? How do we map the asset classes of a plan's own investment policy to the asset classes of investment forecasters' CMAs? If we use asset-smoothing for valuation assets, should we use market-driven expected returns or smoothed/average versions of them?