Description: Highlights from the Social Security & Mortality panel at the 2017 Living to 100 Symposium - actuaries responsible for projections in the US, Canada and the UK.

Description: Discussion of the Society of Actuaries Report on Mortality Improvement Survey from March 2003 survey from the summer of 2000

Description: This article focuses on these Key Points - The Situation: Mortality Improvement assumption is not a simple equation. What Could Happen: Any mortality improvement assumption is a bit of a ‘bet’ relative to the ongoing penetration of known developments and the introduction of new developments, such as the appearance of the AIDS epidemic. What Needs to Happen: Better understanding of Mortality Improvement assumption, final assumption, and best estimate approach.

Description: In the June 2014 issue of Product Matters, Doug Robbins of Pacific Life authored a fascinating article entitled “Velocity of Diversification.” Therein, he proposed a pricing measure that describes the “…speed at which confidence in profitability is attained”. How can stochastic modeling of mortality be applied to analyze the velocity of diversification, and how would that measure be affected by volatility in the underlying assumptions?

Description: This article discusses the comparative results from the SOA Mortality Table Structure survey. From Reinsurance Section News, November 2007, Issue No. 61.

Description: This article reviews studies find that the mortality gap has significantly reduced – though not eliminated – the overall progressivity of Social Security.

Description: Milliman was engaged by the SOA to conduct two Accelerated Underwriting (AU) surveys, one of direct companies and another of reinsurers. The surveys explored actual results from 2019–2021 and expected results from 2022.

Description: Recently completed experience and research studies from the SOA.

Description: Today, a plethora of tools and approaches exist to develop lifetime projections of long-term care (LTC) business. Within these tools lie two distinct approaches to project mortality; namely, by using assumptions that are applied: (1) to an all-lives exposure base, or (2) separately for disabled- versus active-lives exposure bases. Using disabled versus active mortality allows for more granular modeling of the two different cohorts that exhibit dramatically different mortality. And so, when using an all-lives mortality assumption, is the projection missing important details about the appropriate mortality level? This article seeks to not only answer this question, but also answer how a “traditional” all-lives mortality assumption can be impacted when you introduce a combination of the two modeling techniques into the development of a mortality assumption. To answer these questions, we examined the experience and results developed from one c ompany as an illustrative case study.