Description: This suite of projects analyze four decades of U.S. mortality disparities across socioeconomic groups, providing data-driven insights using life tables and demographic techniques.

Description: Keynote Speaker: J.D. Kleinke, Author and Medical Economist J.D. Kleinke has been instrumental in the creation of four health care information organizations; has served on the Boards of several health care companies; and spent many years advising both sides of the political aisle on non-partisan, practical approaches to health policy and legislation.

Description: Examines complex structured assets used by life and annuity insurers, including structures, risks, performance in crises, and implications for capital and asset-liability management.

Description: Seeking researchers to analyze long-term risk trends via survey data and develop engaging report formats for broad audiences.

Description: Improve your financial reporting knowledge with this SOA e-course. It will help you understand financial statements and regulatory capital requirements.

Description: The Society of Actuaries announces the release of a new report that will provide a resource for the insurance industry to learn about image recognition technology. Authored by Kailan Shang, the report explores the current status of image recognition technology, models for image recognition, and various possible applications of image recognition to insurance.

Description: It's your first full day of productive learning! Start it off right with a delicious breakfast and warm welcome.

Description: The use of data to assess risk is not new to the life insurance industry, but has historically been limited to leveraging structured data. Join us as we discuss the next frontier: unstructured data.

Description: Healthcare risk adjustment methodologies have become increasingly complex, largely due to the belief that complexity enhances accuracy. However, a disconnect exists between how healthcare risk is assessed and how payments are adjusted. This article explores the stark difference between measuring the accuracy of a risk assessment model at a member level, and that of a risk adjustment mechanism at the health plan level. I show that a highly simplified approach that does not use diagnosis codes can have similar accuracy on risk adjusted payments to models that do. These findings suggest that accuracy is not a barrier to developing simpler risk adjustment methodologies, which can improve payment accuracy and reduce the financial incentive for coding diagnoses.