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: Discuss various analytics and use cases using payer published reimbursement contract data under federal mandate of Transparency in Coverage. These contract rates are publicly available in machine readable format (MRF) starting July 1, 2022, and refreshed monthly. By attending the session, you will have a better understanding of the state-of-art development and analytics using MRF data, including the technologies used to extract and analyze huge MRF files, validation and limitations of published rates, and their use cases.

Description: "Discuss research conducted at the Cleveland Clinic to understand levels of anxiety and depression across Northeast Ohio. Specifically, learn how advanced analytical techniques were utilized with social media data from Twitter and Google to estimate the need for psychological support by geography and overtime. The panel will go further to demonstrate how natural language processing (NLP) and machine learning could be used to make operational decisions at a leading health care provider."

Description: Explore a brief overview of federal income taxation of health insurers and selected technical tax topics during this panel discussion webcast. You'll dive into the evolution of heath insurers and healthcare-related services and the arising questions regarding their risk-bearing activities, as well as whether or not certain types of healthcare businesses qualify as "insurance companies" for federal income tax purposes. Also, expect to learn about loss reserves for guaranteed renewable contracts and how they can affect insurance company tax status. This webcast will continue to highlight how the IRS challenges loss reserves of health insurers as the panelists review the status of tax cases, IRS guidance and recent IRS activity regarding "margins."

Description: Dive into the released enhancements of MIM-2021, including a run through of its purpose and evolution, during this webcast. The Mortality and Longevity Strategic Research Program plans to release these updates in October. You will leave this webcast with an understanding of how to create a mortality improvement scale using the necessary steps and tools. Learn how to use MIM-2021 through instruction and demonstration that will properly prepare you to utilize this tool. There will be time for questions where you can ask about the projection process or provide feedback.

Description: Disability and group life products can pose challenges for the valuation actuary due to the long-tail runout of traditional disability and group life waiver of premium claims, and due to the sensitivity of the reserves to small changes in assumptions. This session addresses a number of issues of interest to actuaries who work with these products, including recent experience trends and the new 2023 Group Life Waiver Valuation Table. Attendees will learn about best practices in the valuation of these products.

Description: The session will discuss key areas of statutory reserving that contain some ambiguity or areas where there are simplifications being used in the industry. The content will be based on current practices used in the industry and possibly accompanied by an industry survey to be completed prior to the session. The focus will be heavily on CRVM, AG38, and PBR statutory frameworks and will include a Q&A portion for audience participation. Some potential topics will be reinsurance modeling, DBO3/ROP treatment, segmentation, deficiency reserves, system differences and multi-tiered designs

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: Analyze the market volatility behavior in context with historical extreme events during this webcast. There will be a focus on understanding volatility clustering and relationships, both contemporary and temporal, in addition to key economic and capital market variables. You'll also learn from a comprehensive attribution analysis, which uses economic data, asset data, retail investor data and event data as explanatory variables. Dive into investment-related Reddit comments used to capture features of retail investors and tree-based models, including Random Forests and Gradient Boosting Machines to justify changes in volatility modeling. Expect this webcast to end with an example of liability-driven investment optimization that illustrates the potential impact of increased volatility assumption, how to adopt stochastic volatility models, how to reflect volatility clustering and return jumps.

Description: We're introducing a new “cutting edge” wealth accumulation predictive framework, the predictive equity analytics (PEA) for life products (annuity, insurance, group employer benefits, retirement plan). PEA enables life insurers and policyholders to avoid market downturns and capture returns from equities (stocks, ETFs and mutual funds) predicted to increase in next one to three trade days via fund prediction advisory by insurers, additionally by individual policyholders. The capability does not exist in insurance markets nor in financial intermediary markets.