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 'answers' to any questions the actuary might ask of it.

Description: Sarah Rockhill, MPH, Geospatial Epidemiologist at CDC will discuss the latest in the Public Health 3.0 model in which leaders serve as Chief Health Strategists, partnering across multiple sectors and leveraging data and resources to address social, environmental, and economic conditions that affect health and health equity.

Description: This session will explore governance considerations for insurance carriers leveraging machine learning models. During the session, panelists will share insights on the evolving predictive model and AI regulatory landscape, as well as techniques for developing or enhancing AI governance frameworks that comply with emerging regulatory guidance.

Description: How well can we predict ACA HHS Risk Adjustment Data Validation (HRADV) results? Put another way, how susceptible is the current HRADV process to small sample volatility? What potential remedies are there to the current model's shortcomings? Every plan, regardless of how many members they have above the 1000 minimum, count equally if they qualify for an HRADV with how the result is interpreted, and the subsequent HRADV risk transfer ($330M in 2023).

Description: This session will discuss what is meant by 'bias' and 'fairness', the regulatory environment, how to identify and mitigate bias in models, and proper oversight and governance of model building. The session will include a case study.

Description: Predictive analytics and technology have dramatically enhanced insurers’ ability to write Life Insurance policies without the need for medical exams in the past 10 years. Rapid, remote access to richer, digital health data has helped better identify higher risk individuals and capture anti-selective behavior, allowing policies to be issued at rates more competitive than traditional guaranteed issue products. Yet this underwriting technology is still new and rapidly evolving, challenging insurers to develop appropriate assumptions for new issues as well as the valuation of in-force blocks.In this session, we will look back at mortality and persistency experience data gathered in Oliver Wyman’s industry-wide VM-51 experience study, one of the most comprehensive data sets to include limited underwriting business in recent years. We will see how experience held up against expectations, and what possible trends we see across various policyholder characteristics. We will then zoom in on the impact of risks that would be considered uninsurable from a full underwriting perspective, and the impact that these lives are having on in-force blocks from a valuation perspective. Finally, we will look forward, examining some of the newest innovation in the limited underwriting space, as presenters discuss methods and considerations for the validation, assessment and implementation of new underwriting offerings, such as Milliman Risk Score and LabPiQture™.Attendees will enhance their understanding of the impact of underwriting changes over the past decade, as well as have a view of how things may change in future years as the industry becomes progressively more capable of assessing risk without the need for medical exams.

Description: Over the last 10 years, Artificial Intelligence (AI) healthcare foundation models have made tremendous strides when it comes to delivering highly accurate predictive results when compared to traditional models. This is especially true for models developed and trained on the ‘language’ of healthcare. When trained on healthcare data sequences (claims, EHR, CPT, ICD, pharmacy, lab, etc.), these models deliver far more accurate results as to WHAT beneficiary-level health events will occur and even WHEN they will occur.

Description: The life insurance industry is facing a rapidly changing landscape, with several emerging risks demanding the attention of actuaries and industry professionals. While actuaries commonly examine historical experience to guide our estimates of future outcomes, we are often faced with evidence that some of the forces that dictated past performance no longer persist and in some instances may even turn into contrary forces. This session aims to explore the key challenges and opportunities presented by risks with limited historical context, and discuss strategies to effectively manage them. We will delve into critical emerging risk areas, such as interest rate and asset risk, the rise of AI, climate change, predictive analytics in underwriting, and more. The session will feature speakers who are experts in managing risk, as well as results from interviews with top corporate risk professionals, such as CROs and ERM leads from major insurance and reinsurance companies. Attendees will gain valuable insights into emerging risks and practical strategies to navigate the evolving life insurance landscape.

Description: This session will discuss essential theories and practical applications of deep learning for actuaries. Participants will gain insights on leveraging these cutting-edge techniques to enhance actuarial models and drive predictive accuracy.

Description: This session will share insights on how to effectively evaluate and price the management high-cost claimants from three views: clinical, payer, and provider. Can predictive analytics help with underwriting? What are effective clinical mitigation strategies? Can VBC arrangements be used to reduce total cost of care, and how can that be priced in?