Description: This study presents a comprehensive approach of real option analysis to climate adaptation policies aiming to mitigate flood risk. The framework focuses on pricing catastrophic risk in coastal regions, allowing for economic growth and uncertainty in the mean sea level rise.

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 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.

Description: This collection presents three essays analyzing the multifaceted impacts of AI and LLMs on retirees and retirement professionals, blending quantitative, philosophical, and forward-looking perspectives.

Description: The global doubling of information content has been estimated to occur at a rate of every 12 hours, and conceivably faster. This ‘infowhelm’ is a double-edged sword for insurers. While maximization of the global knowledge base can fuel innovation, it can occur only in the context of reasonable cost for a higher benefit. The insurance industry – underwriters and actuaries in particular – are challenged to constantly examine new innovations that promise to raise confidence and protective value for insurability, within legal limits. This report aims to be the first of several to alert the SOA of promising innovations for the profession.

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: The objective of this paper is to provide a practical guide with concrete case studies to help actuaries implement artificial intelligence and machine learning (AIML) to help accelerate the speed of analysis for life and annuity actuarial modeling. We do so by researching the existing literature regarding AIML and its applicability to stochastic modeling and exploring case studies.

Description: This report is a discussion of the significant challenges involved in attempting to research with robust analysis the impact of disparities in health care access, cost and quality of care on financial wellness across racial, ethnic, socioeconomic and other demographic groups in the U.S. for reasons other than or in addition to income, wealth or employment status.