Description: Frequently Asked Questions about the SOA's University-Earned Credit (UEC) program.

Description: The document explores how AI-supported underwriting influences mortality slippage in accelerated underwriting and evaluates real-world applications, risks, and industry impacts.

Description: This interactive webcast uses case studies and a new framework to show how retirement plans and practices can be fitted to best serve organizations’ missions and objectives, guided by data and analytics measuring their workforce impact.

Description: This webcast will update listeners on the financial status of the U.S. Social Security program, describe half a dozen widespread myths, and discuss the reality of "waste, fraud and abuse" in the program.

Description: CMS has pioneered innovative strategies for Medicare and Medicaid to address Social Determinants of Health (SDOH). Employers can learn from these models to implement similar approaches for their employee populations. Learn how employers can understand and address SDOH, the incentives for doing so, and the broader benefits.

Description: This study evaluates how integrating social determinants of health (SDOH) into Medicaid risk adjustment models impacts financial outcomes for ACOs/MCOs, offering foundational insights for policymakers and actuaries.

Description: This document explores how climate change impacts retirement planning, investments, housing, and health across policy and economic dimensions through essays and funded research.

Description: This report outlines a framework for interpretable machine learning algorithms for detecting fraud in health insurance. It discusses the challenges of using black-box machine learning models in the insurance industry and proposes various interpretable methods to enhance transparency and understanding.

Description: This report examines the demographic, medical, and pharmaceutical profiles of HIV-positive individuals in Medicare and how their needs differ from the general Medicare population.

Description: A multi-author collection exploring how catastrophe modeling can be adapted to life and health insurance, particularly in response to the 2025 Los Angeles wildfires.