Description: Web3, Crypto, and Generative AI are no longer just buzzwords as they reshape the tech landscape. Viewed by many as unconventional technologies, their potential to now revolutionize the insurance industry cannot be ignored. Join us for an enlightening session that delves into the transformative power of these technologies and their current applications within the insurance sector, as well as discover how forward-thinking insurance companies are embracing these tools to unlock innovation and challenge the insurance status quo.

Description: EHRs are a source of digital health data that present both an opportunity and a challenge for insurers. These rapidly maturing data sources contain valuable insights and can create a faster, better applicant experience, but the costs can quickly add up. Listen to panelists discuss various use cases for EHRs, their protective value in relation to traditional underwriting approaches, and other considerations for expanding EHR use. We will take a look at how using EHRs could promote a smart ordering approach allowing us to be quicker and more cost effective, all while understanding the impact to mortality.

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

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: Have you ever struggled to make business decisions because your internal mortality studies lack credibility? Have you ever launched a new product or initiative and wanted to understand mortality risk before claims experience emerged? This session will discuss how predictive models can help actuaries enhance their traditional mortality studies with leading mortality leading indicators to be more nimble to market demands.

Description: There has been a shift in finance and actuarial modernization in the insurance industry over the past 2 to 3 years. Discuss the latest trends in the marketplace and the key changes we see actuaries and actuarial employers needing to make in the future of work. Learn how actuarial work is evolving and about the changing manner in which work is performed. Brainstorm specific ways actuaries can thrive in the future, including performing more strategic work.

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: "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."