Description: Highlight the importance of assigning granular policyholder behavior assumptions to cohorts of a block that behave differently for universal life products. The case study assumption testing will focus on potential cash flow impacts of surrender, lapse and premium persistency behavior— individually and combined. The case study will be based on testing data related to typical demographics for a ULSG product and not on any specific company data.

Description: The need for LTC services is projected to increase as the general population ages. Many LTC carriers have helped meet this demand but are looking for new ways to mitigate underpriced risks and to improve policyholders' quality of life. Aging-in-place interventions can be an effective method to mitigate financial risks and improve health outcomes. However, many carriers face challenges to prove financial benefits/ROI, quantify program effectiveness, or simply don't have clarity on how to start and implement these initiatives effectively or operationally.

Description: Predictive analytics techniques help actuaries and their companies make better decisions in managing their business.

Description: CMS has required hospitals to publish their chargemaster rates, cash discounts and negotiated rates as of January 1, 2021. Our panel is comprised of a data strategist, an executive from a large hospital system and an actuary.

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: "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: Explore insights to non-traditional actuarial roles from a panelist with experiences in data science, finance and entertainment. The panelist will be sharing their perspectives on their non-traditional actuarial role in relation to the actuarial profession.

Description: Generative AI has become a hot topic since ChatGPT became one of the most well-known Machine Learning models ever. There are many different generative models out there, and in this presentation, we explore the possibilities, benefits, reservations, and alternative use-cases of using Generative Adversarial Networks to generate health insurance claims data.

Description: The session will provide attendees with a framework of what goals to think about when building and utilizing a predictive modeling framework in New Sales and Renewal Underwriting mid-market, level funding, balance funding, and self-funding group health business.

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."