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: 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: This project models and forecasts premature cardiovascular mortality, with a focus on two main risk factors - obesity status and education attainment level. It investigates the role of obesity and education in determining age-sex-specific premature cardiovascular mortality.

Description: This report explores the behavioral factors influencing insurance purchase among Gen Z and Millennials for forward-looking purposes, leveraging a rich dataset spanning 22 markets across six continents.

Description: Hear about some applications using quantile regression-derived individual and group claim distributions and how they may be used in various traditional health care modeling situations. The session will not focus on the technical use of quantile regression to obtain estimates of claim distributions, but more so is intended to focus on the uses of the distributions in actuarial modeling and the additive framework in assessing claims distributions. Presenters will show how actuaries can better assess risks by focusing on the individual- and group-specific claims distributions as opposed to traditional point-estimate-centric models.

Description: Come learn from a panel of LTC industry professionals as they discuss setting goals for LTC wellness, measuring success of the programs, and integrating them with other LTC process (e.g. assumption setting) in an effort to create sustainable programs.

Description: There is increasing industry interest in applying behavioral science to optimize product design decisions and improve outcomes for both insurers and their customers. With a growing set of options within product designs, predicting consumer behavior continues to get increasingly complex. Join this webcast for an overview of key behavioral economic concepts. Key insights will be shared from two SOA projects that explore how behavioral economic techniques can be used in various parts of the life insurance new business process and inforce management.

Description: Article describing content and key takeaways from experience completing the SOA PA Certificate

Description: "Hear AM Best rating analysts discuss the rating process and the best capital adequacy ratio (BCAR), a quantitative foundation for AM Best’s assessment of carriers’ financial strength. The benchmarking of BCAR scores for various company types (national MCOs, BCBS plans, smaller regional plans) will be discussed. Look at how changes in investment and underwriting environment impacted the results over the past several years. Further, AM Best’s view of the quality of capital and various factors impacting the overall assessment of the balance sheet strength will be discussed. By attending this session, you will: Gain an understanding of AM Best’s building block approach to ratings and how the BCAR results fit into the process. Learn the differences between RBC and BCAR models. Learn how various factors—e.g. liquidity, financial flexibility, financial leverage, the org structure—impact the final AM Best assessment of health companies' capital."