Description: The expanded use of predictive models, algorithms, and external consumer data has drawn the attention of the life insurance industry to the matter of fairness and bias in underwriting, including proposed regulation in states like Colorado and New York and a draft NAIC Model Bulletin. Insurers will benefit taking a proactive approach to verifying that their practices, both current and future, do not result in unfair discrimination. This session will help attendees in understanding potential requirements and reporting insurers must address, techniques and tools for evaluating bias, and challenges in doing so. At the conclusion of the session, attendees will be able to: - Understand proposed regulation and practices regarding fairness and bias - Identify tools and techniques for evaluating bias in their processes - Discuss the challenges the industry will face in addressing fairness in underwriting

Description: Session 3A: State-Specific Enrollment Modeling - Understanding Disenrollment Trends and Population Dynamics

Description: Almost half of recent FDA approvals have been for orphan drugs. This trend is expected to continue in 2023. Orphan drugs are priced more than 30 times higher than non-orphan drugs, therefore their cost burden in aggregate has the potential to overwhelm the budgets of payers, especially that of smaller health plans. Hear how the cost impact analysis for smaller health plans require different considerations than for a larger health plan when modeling the impact of rare diseases.

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: 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: Can you answer the question, 'What are the chances we will lose more than $1 million?' without saying, 'It depends'? If you miss a projection, can you estimate the chances that the miss was due to random variation versus an incorrect projection? Listen in as presenters demonstrate how to answer these questions and others using total risk analysis. Total risk analysis includes quantifying both the random variation risk and the projection risk associated with actuarial functions like pricing and reserving. After attending this session, you will be able to describe total risk analysis and to apply to many health actuarial functions.

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.