Description: Article is about a new course developed in actuarial science taught at Columbia University. Course covers traditional health actuarial projects but is expanded to cover issues for a health insurance company executive (e.g., CEO, CFO, etc.). The course also includes studying of other international healthcare systems plus healthcare reform (PPACA). Students were involved in research projects, including healthcare reform and international health systems. Students also had to teach a course as part of their final exam.

Description: An excerpt from a presentation at the 2014 Living to 100 Symposium, this article describes an open-source model for use by actuaries attempting to estimate future health benefit costs, discusses its strengths and limitations, and projects that spending for those age over 65 will take more than $13 trillion by 2055, about 50 percent of total medical expenditures.

Description: Editor's comments on the publication

Description: Adapting actuarial skills for the new multidisciplinary work of healthcare delivery system reform.

Description: Voluntary health insurance coverage options are described including hospital indemnity, accident plans, disability insurance, and critical illness policies.

Description: Recap of the health sessions at the 2014 SOA Annual Meeting.

Description: This article provides an overview of the Own Risk and Solvency Assessment (ORSA) model act, and of enterprise risk management (ERM). Compared to other actuarial disciplines, health actuaries have not historically focused as much on ERM. But health coverages do have risks that can threaten an insurer's solvency, and ORSA (now in force in most states) will force health insurers to beef up their ERM capabilities.

Description: Disruptive changes will create opportunities for health actuaries as we help health plans develop their overall strategy.

Description: Update on the SOA Health Section's Public Health Task Force's comment on Health People 2030.

Description: This article provides a brief overview of the current state of machine learning (ML) and artificial intelligence (AI) as they apply to actuarial science, including discussion around the integration of actuarial judgment with ML and AI. It concludes with an example of explainable ML through a python tool known as SHAP (SHapley Additive exPlanations), which is able to explain the key drivers behind individual ML or AI predictions.