Description: The aim of this project has three aspects: (1) providing a comprehensive literature review on telematics car driving data analytics; (2) developing various visual tools and a data cleaning procedure for telematics data; and (3) extracting risk factors from individual trips and improving classic classification ratemaking models with those risk factors.

Description: This report identifies and makes recommendations regarding possible exposure measures for pricing and analyzing the risks in cyber insurance. Cyber insurance is an insurance product that is designed to provide protection against the financial consequences of a failure or compromise of an organization’s information system as a result of a cyber event.

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: Summary of an expert roundtable on generative AI in healthcare and health insurance, focusing on use cases, governance, trust, risks, and evolving actuarial skills.

Description: This project examines how companies perform deterministic scenario analysis, focusing on a worldwide IT disruption and its operational impacts. It highlights the use of table-top exercises to estimate direct and indirect effects, leveraging senior managers' experience.

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: 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 presents initial findings from a project investigating the direct and indirect impacts of COVID-19 in the post-acute phase of the pandemic. These findings focused on cause-specific excess mortality in the first three pandemic years, March 2020 to February 2023. In addition, the authors investigated how different mechanisms contributed and could continue to affect mortality rates following the acute pandemic period. The study is based on weekly mortality data by age and cause of death from 2015 to 2023 obtained from the National Center for Health Statistics at the Centers for Disease Control and Prevention, combined with population estimates from the U.S. Census Bureau. Weekly excess deaths by age and cause comparing observed deaths to deaths expected based on pre-pandemic trends were computed. Then the temporal correlation between weekly COVID-19 and excess deaths from other causes at various lags were assessed. The authors developed a theoretical framework to interpret the results of this analysis and understand potential underlying factors responsible for excess mortality.

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: 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.