Description: A Discussion of why female mortality is higher than male mortality at higher ages.

Description: Describes a process for identifying relevant industry experience to credibility-weight with relevant company experience to set modeling assumptions and margins.

Description: This report discusses drivers of U.S. mortality improvement for the short and long term.

Description: Mortality trend risk affects pension plans, life insurers, annuity writers and insurers of workers’ compensation, where the tail claims are mostly annuities. As with many risk sources, mortality risk can be broken down into process risk, parameter risk and model risk. In this paper, process risk, parameter risk and model risk are discussed using the fitted models. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

Description: This study develops mortality ratios, using contemporary population tables and standard industry tables as the base of expected deaths, for 3,037 members 2,811 Fellows and 226 Associates of the Society of Actuaries observed from January 1, 1955 through December 31, 2004.

Description: This is a letter from Jan M. Hoem to Arnold F. Shapiro about Exposed-to-risk considerations based on the Balducci assumption and other assumptions in the analysis of mortality.

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

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 details of being classified as cognitively impaired, particularly the relationship between being classified as cognitively impaired and subsequent mortality experience and the effect of cognitive impairment on mortality rates.

Description: As a life reinsurer, understanding mortality risk is mission critical. Superior models of mortality risk lead to more competitive pricing in profitable market segments and more efficient use of capital. Surprisingly, modern statistical tools such as predictive modeling have not previously been used to model mortality data to gain this edge. In this paper, we present the use of a predictive modeling technique called projection pursuit regression PPR to model mortality data. We first describe the process of creating a PPR model using inter-company mortality data collected by the Individual Life Experience Committee of the Society of Actuaries, and present the resulting model. We then discuss the process of using this model as a starting point for creating a more detailed PPR model of internal company mortality data.