Description: This session will focus on what is new and coming at the Appointed Actuary (e.g., Actuarial Guideline ((AG)) 53, equity testing, etc.). We will provide highlights from the updated AAT Practice Note. A particular focus will be paid to asset classes that have received regulatory attention recently (e.g., commercial mortgage loans, collateralized loan obligations ((CLOs)), etc.).

Description: Session 7A: Integrated Analytics for GLP-1 Therapy Management: Optimizing Clinical and Financial Value

Description: When it comes to climate-related risks, what does the future hold? Will we see a rise in global temperature of 3 degrees Celsius or more, or can we keep it to less than 1.5 degrees? Will we see ambitious climate policies instituted globally in the near future, or will the status quo prevail, leading to irreversible changes to the climate and more severe physical risks? As actuaries, we are accustomed to planning for an uncertain future, but assessing the long-term impact of climate change poses a unique challenge. There are several public institutions developing scenarios like those above and others, which can help actuaries understand and assess the risks posed by a changing climate. Through scenario analysis, organizations can consider a range of possible outcomes to inform decision-making. Scenario analysis can be qualitative or quantitative, and there is no uniform framework that is currently being used by organizations that are studying the impact of climate change on their business. At the end of this session, participants will be able to: - Describe future climate scenarios. - Describe how actuaries can use scenario analysis to assess the impact of climate change and climate policies. - Explain why actuaries might use scenario analysis to understand climate risk.

Description: Our panel will discuss techniques for efficiently modeling assets and liabilities while minimizing the computational expense of full-blown nested Monte Carlo simulation. These techniques include: • Neural networks • Data clustering and selection of representative contracts • Scenario reduction • Proxy functions for estimating the value of complex assets and exotic derivatives embedded in liabilities. Attendees will learn about the theory behind these techniques and implementation practicalities, and we will present case studies illustrating their benefits.

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: The NAIC continues to move ahead with the GOES (generator of economic scenarios) project for the transition to a new real-world economic scenario generator (ESG) prescribed for principle-based reserve and capital calculations. This session will provide a detailed project update from the regulator and practitioner perspective. An overview of model details and methods that have been decided, what remains to be determined, and the projected timeline for implementation will be discussed. Recent developments such as the industry field test and NAIC model office testing to assess the potential impact on reserves and capital will be discussed as will a review of the implementation of the stochastic exclusion ratio test (SERT). We will also discuss the components of the ESG model and the associated scenario sets the NAIC has exposed with respect to target and acceptance criteria. At the conclusion of this session, attendees will understand the current status of the NAIC GOES project and its projected timeline and will be able to describe the components of the model and how the model has been calibrated and validated. Attendees will also appreciate the remaining challenges in bringing the model to full implementation.

Description: Session 6D: Breaking Barriers - A Multidimensional Approach to Behavioral Healthcare Access