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: 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: In this session, we will delve into the application of survey data analytics to extract valuable insights from public survey datasets. The discussion will start with the basics of survey design, sampling practices, and estimation of key statistics. Three case studies will be presented: one in the realm of population health and another in medical economics, both demonstrating the use of either SAS or R to perform the survey analytics.

Description: The National Association of Insurance Commissioners (NAIC) project for the transition to a new real-world economic scenario generator (ESG) prescribed for principle-based reserve and capital calculations selected a vendor in the fall of 2020. The American Academy of Actuaries and other interested parties have been assisting the NAIC in this transition. Over the past four years, extensive analysis, model validation, and comparison work has been done. There have been many updates on the project but few opportunities to obtain an in-depth understanding of the new features of the new ESG and its impact on reserves and capital. Practitioners are very interested in the questions: - How different are the scenarios from the Academy/Society of Actuaries ESG and the new NAIC ESG? - How different are the models and techniques used to create the scenarios in both generators? - Is there a significant difference in capital and reserve requirements for model office tests between the two ESGs? - Are there concerns with the new NAIC ESG? The session addresses each of these questions in detail. At the conclusion of the session attendees will appreciate the key differences between the model structure and scenario performance of the two ESGs and will understand the differences in capital and reserve requirements for model office tests across the two ESGs. Attendees will also learn how the initial yield curve fitting differs between the models, about fund mapping considerations, and the interpretability of parameters in the new NAIC model.

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: 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: 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: In this session, we will do a deep dive into the potential of large language models; we will explore how new AI tools can be combined with traditional modeling to automate aspects of risk assessment. Industry experts focused on operationalizing this technology will talk through their approaches, lessons learned and demo use cases. We will also share insights around creating a robust AI strategy based on business objectives and regulatory considerations. Learning Outcomes: • Applications of large language models in risk assessment use cases • Regulatory considerations around the use of generative AI