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 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."

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: 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: Come discuss modeling costs and its governance, as well as introduce some platforms for more granular asset modeling.

Description: The American Academy of Actuaries Health Solvency Subcommittee and the NAIC Health Risk-Based Capital (E) Working Group are in the process of analyzing and reviewing the H2-Underwriting Risk component and the managed care credit calculation in the health risk-based capital (HRBC) formula. Come learn what changes are ahead! While RBC is designed for solvency issues, also come hear about other ways health insurers manage their capital and surplus levels. Are others doing something you should try?

Description: For actuaries interested in term life insurance, add this webcast to your calendar. Our panel of experts will discuss term life insurance from the following perspectives: Pricing Product development Reserving Risk management They will also discuss exploring data visualizations using Tableau.

Description: This training session will include lessons learned in 2021 and revisit a presentation given last year, discussing the new Actuarial Standard of Practice (ASOP56), which fills in the gap for guidance on Actuarial Modeling. Prior to ASOP 56, actuarial judgement reigned where standards and guidance lacked. Learn the foundation for designing, developing, modifying and validating a model according to SOA guidelines. By attending this session, you will: • Learn how to identify model’s intended purpose. • Strategically extract input data and assumptions. • Use the level of detail required in output data as a consideration for model design. • Choose from SOA recommended validation techniques.

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 answersto any questions the actuary might ask of it. However, in this particular field, it is often worth taking a step back, and ensuring that we recall and consider the warnings that come with each of the statistical procedures we might attempt to use. This will allow us to ensure that we guard ourselves against drawing inappropriate conclusions, which could come back to haunt us later. By attending the session, you will: - Avoid drawing inappropriate statistical conclusions, while doing their work. - Leave informed about the types of pitfalls that exist and where those might come into play in actuarial work."

Description: The surge in interest rates over the past few years presents opportunities for life insurers to enhance their Asset and Liability Management (ALM) positions through asset rebalancing to narrow the duration gap and secure higher risk-adjusted yields. This webcast introduces a robust, efficient and readily-executable, liability-driven strategic asset allocation (SAA) framework and implementation considerations. At the conclusion of the webcast, attendees will learn about the key objectives and practical considerations for a successful SAA.