Description: This will be the most fun you'll ever have learning about applying health status based risk adjustment methodologies in the real world! Quantifying relative differences in the use of finite healthcare resources – due to differences in health status – isn't just fun, it's the very heart and soul of ASOP 45! Get ready for a rollercoaster of statistics, probability, and a few puns that might just make you laugh harder than a data outlier!

Description: This interactive session will compare industry predictions versus what happened with the unwinding of continuous coverage requirements in Medicaid that were first put in place due to the COVID-19 public health emergency. The audience will be able to participate in a guessing game as the panelists review how enrollment could be stabilizing.

Description: The video features Chris Onken, a principal and consulting actuary at Milliman. It focuses on identifying equity volatility risk and fundamental strategies to manage it. The discussion covers various types of volatility, including realized, implied, constant, and stochastic volatility, and their applications in projecting asset price volatility. Additionally, the video explores static and dynamic hedging techniques for managing volatility risk. This video provides valuable insights for professionals in the insurance industry.

Description: Unique Data Analysis Techniques to help plans find that 1% edge in opportunity and value. The discussion will vary from a conversation on members who are at high catastrophic risk to provider networks and referral patterns. The goal of the session will be to help show ways plans can create savings through outside-the-box techniques.

Description: In this session, actuaries and data scientists will describe how the Cleveland Clinic has utilized large language models to support caregivers with difficult end of life conversations with patients and their loved ones. While artificial intelligence is often prefaced with words of caution, it can also be harnessed for good.

Description: Model Risk Management at insurers has been an established practice for well over ten years now. Until recently, a primary focus of model governance was on actuarial models. The Gen AI evolution we’ve experienced in the last two years, has shone a spotlight on “all things AI”. Along with the increased capabilities AI brings, there is increased concern for the proper use of these models, protection of IP, safeguarding of PII, and overall security. This session will bring together a diverse panel to discuss how we can adapt our established practices for these new models to minimize the risks that come with Gen AI and help our organizations optimize the benefits of AI.

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, collatoralized loan oblications (CLOs), etc.)).

Description: Health Insurers commonly face challenges regarding data including: Limited knowledge of how to handle large datasets Lack of a common language to discuss data challenge Limited understanding of how to use data and analytics to inform and facilitate decision-making We are seeing a turn in the market to use data and analytics to cultivate an insights-driven culture. But we are noticing a disconnect between the actuaries that are able to glean these insights and the new technologies required to handle the amount of data available to them. During this session you will look at moving from MS Excel to more advanced data handling and modeling tools (e.g., Alteryx), visualization tools (e.g., Power BI & Tableau) and expanding utilization of these tools and analytics. By attending this session, you will: • Understand data and analytics (D&A) concepts, methods, and fundamentals to help drive value. • Gain an appreciation for data privacy, protection protocols, and related ethical considerations when working with data. • Be able to identify of how to use data and analytics to inform and facilitate decision-making.

Description: En el seminario hablaremos sobre los nuevos flujos de tarificación, y nos enfocaremos en la inclusión del análisis geográfico en el proceso de modelado para optimizar la extracción del riesgo en modelos de Machine Learning.

Description: The panel will discuss several product challenges including modeling hybrid/combo long-term care products, modeling benefit options that are difficult to model using deterministic methods, modeling current situs of care versus original situs of care, monitoring assumptions, streamlining experience studies within the Long-duration Targeted Improvements (LDTI) framework, and model decision making with limited credible data. The panel will then move into solutions observed to address some of the aforementioned challenges and the future of long-term care modelling