Description: Virtual - Session 3A: But We have ChatGPT at home... Or Operationalizing Generative AI

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: 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. However, in this particular field, it is often worth taking a step back, and ensuring that we recall and consider the warnings that come with each of the statistical procedures we might attempt to use. This will allow us to ensure that we guard ourselves against drawing inappropriate conclusions, which could come back to haunt us later.At the conclusion of the session, attendees will be able to avoid drawing inappropriate statistical conclusions, while doing their work. They will leave informed about the types of pitfalls that exist, and where those might come into play in actuarial work.

Description: This session will cover everything you've wanted to know about GLMs but were too afraid to ask. How do these generalized models relate to the plain linear regression, and how can we be sure that the models we implement are appropriate for the task at hand?

Description: Session 2F: Artificial Intelligence and Actuarial Modeling - Ask an Expert!

Description: Session 7B: Health AI in Practice - Lessons From Top U.S. Health Systems

Description: The insurance industry hinges on accurate actuarial assumptions. This session explores how insurers can leverage AI/machine learning (ML) to unlock greater accuracy and granularity in assumption setting, while effectively managing risks, implementing strong governance frameworks, and meeting validation expectations from regulators.

Description: "Risk adjustment is an important aspect of establishing appropriate capitation rates. When a set of services is carved out for specific management under a sub-capitation, establishing appropriate adjustments for varying levels of underlying risk is more complicated. Dive into analytical and operational challenges and resolutions as presenters share more about developing a risk adjustment model specific to the carve-out of behavioral health services through a real-life case study. You will further explore a definition of the use case, availability of historical data sets, selection of the modeling approach, learnings from the iterative modeling process, and the potential application of the results. After attending this session, you will be able to: Understand why a behavioral health specific-risk adjustment model has business value Understand the unique challenges related to developing risk adjustment on member and cost subsegments Understand the impact of trade-offs between model complexity and interpretability in the context of implementation Increase awareness of data nuances and requirements unique to this type of project Describe analytic techniques that can be investigated for use in projects of similar business objectives"

Description: As health insurance self-funding becomes more popular among smaller group sizes, risk is evolving for the MGUs, stop loss writers, and reinsurers that cover them. Assessing more small groups at lower attachment points means rethinking traditional stop loss underwriting techniques.

Description: Risk adjustment is widely used by public and private payers in value-based contracting and other risk-sharing arrangements aimed at addressing population health. Addressing social determinants of health (SDOH) and improving equitable outcomes is one of the aims of population health. Could SDOH be incorporated into risk adjustment? If so, how and how effectively? What implications might this have for market participants? In this session, Joan Barret, FSA, MAAA, from Axene Health Partners will begin by presenting background and findings from the 2022 SOA research report 'How Does Where you Live Impact Your Health?.' This is a question addressed through deep literature review and quantitative analysis, followed by an exploration of strategic approaches and barriers for payers and providers that wish to address SDOH inequities. Then, Rong Yi, FSA, MAAA, and Jeff Milton-Hall, FSA, MAAA, from Milliman will present their new SOA-sponsored analysis regarding integration of SDOH factors into Medicaid managed care risk adjustment. They will cover risk adjustment design considerations, policy and operational considerations for actuaries and states that wish to integrate SDOH into risk adjustment, simulated variation in financial outcomes for risk-bearing organizations when such integration occurs, and variation in the relationship of SDOH factors and morbidity to modeled risk across programs and populations. At the conclusion of the session, attendees will leave equipped to think critically and act effectively with respect to SDOH in their own work, from measurement and analysis design to actuarial interventions.