Description: The IRS released the details of a program to allow life insurers to correct inadvertent failures to comply with the modified endowment MEC rules under section 7702A of the Internal Revenue Code the “Code”.

Description: A review/highlights of the report on US population mortality improvements for the 4 years up to 2001

Description: The Mortality Studies Working Group MSWG of the Society of Actuaries was created to review the issue of the frequency of the standard ordinary individual life insurance mortality experience studies and the published reports of those studies. This report discusses these issues and makes recommendations.

Description: This article gives an overview of the first Applied Actuarial Research Conference AARC.

Description: This article discusses an update to a survey that focuses on how companies are trying to price for and understand the risks inherent in the products they sell.

Description: This article presents the results from 1998 Munich American survey of Life Reinsurance production.

Description: With the medical insurance market experiencing health care inflation of 10 to 13 percent and medical costs averaging above $8,000 per year per employee less for single employees and more for employees with families, employers are looking at alternative options. Politicians and regulators are as well.

Description: Attitudes on strategy and risk in reinsurance vary according to specific environmental circumstances for most companies. This article is an extract from the 2007 surveys in the U.S. market.

Description: Feature article that contends in order for actuaries to thrive, technology needs to be central to company strategy and actuaries should view coding as a core competency—equal to or more important than other “traditional” skills.

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.