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: In this webcast, the presenter will cover mental health in insurance products. Learn about the public’s increased interest in mental health and the ways insurers can meet this demand, how we can embed such offerings in insurance and how to successfully expand mental health coverage to those in need. The second speaker will cover how cancer and the simplified issue insurance market has evolved in Korea. In this session, the speaker will share the ease of selling / onboarding, thus allowing a greater penetration of products.

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: Renal disease is a very heterogenous medical condition whose end state is dialysis, a recurring cost intensive medical condition. Dialysis cost is one of the major medical cost drivers in USA. Insufficient and improper care management in the early stages of CKD (chronic kidney disease) leads to early incidence of end stage renal disease (ESRD) among CKD patients which not only leads to high medical costs, but also reflects poor health quality.

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: Get an in-depth explanation on how data scientists and actuaries can work together to battle the issues arising from big data. Join our focus on the insurance industry as a whole and look at data scientists' and actuaries' roles from the views of different areas of the industry. We will also focus on how data science can be a valuable tool for actuaries. After the session, you will be able to understand: - How developments in data science are shaping Insurance Industry practices - How data scientists can help actuaries - How data science can be used to improve analysis of industry experience

Description: The actuarial profession is changing rapidly and the skills needed to be successful are also changing. In this session, we will debate key topics and questions, such as: What is the predominant future career path for actuaries? Specialists or generalists? Does the actuarial profession have a meaningful value prop outside our core domains e.g. insurance? What skills are needed for the actuary of the future? What’s the best approach to improve actuarial storytelling to non-actuarial audiences? What’s the role of actuaries to managing and predicting emerging trends and assumptions? What is the role of actuaries in relation to development and ownership of advanced modeling? Is the future actuarial profession smaller/larger than it is today? In either case, how does it differ from today in terms of make-up, deployment, function, purpose, value add, etc.?

Description: Creating a predictive model is one thing, but then taking that model and getting full stakeholder buy-in is another. Learn from presenters' experiences and insights on proving model effectiveness in the long-term care (LTC), life and annuity spaces. Explore various topics such as feasibility, model transparency, fairness and equity, ROI measurement, data assets and compliance, infrastructure and more. By the end of the session, you will gain an understanding of how to navigate the challenges of proving the effectiveness of predictive modeling and obtaining management buy-in.

Description: Cover a variety of topics centered around the unsupervised learning technique called 'clustering'. The presenters will take you through a wide array of topics that range from 'What is Clustering?' to 'How can I use this technique to solve problems I didn't even know existed?' In the health care industry, everyone is looking for an edge in reducing total spend-cluster analysis finds the unambiguous in an ambiguous framework of results.

Description: Predictive analytics projects in the life insurance industry are oftentimes set up as research ideas leading to intangible or unusable results for the practicing actuary. What if you could change that? What if the research turned into development and practical use through leveraging valuable resources properly? Learn how to structure predictive analytics projects, and about the project team, so there will be actionable takeaways for the business unit. Specific areas of value for you include underwriting, experience studies, assumption-setting, and product development. Watch as presenters talk through real case studies on turning a predictive analytics project into workable solutions rather than a resource drain, an unclear goal, or a misunderstood business need, which are some of the pitfalls of a poorly planned project.