Description: Recent advances in mortality modeling have enabled applicant-specific quantification of mortality risk in life insurance applicants using laboratory and physical measurements. Relative to more conventional underwriting, this approach identifies a small population of significantly under-priced policy and a much larger, cross-subsidizing group whose risk is presently overestimated. Applicants placed in the highest percentile by this scoring methodology are more than 10X as likely to die in a given period as their demographic peers, and represent approximately 5% of short-term claims. Implications for product design are substantial in both the short- and long-term, as the possibilities for adverse selection grow, and the opportunity for fully individualized pricing raises the more distant prospect of products with as many rate classes as applicants.

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: Future mortality improvement has been a common assumption in life insurance and annuity pricing and forecasting for many years. However, experience over the last decade suggests this might be an assumption that merits reconsideration in the United States. This article will take a closer look at factors that impact mortality improvement trends and discuss why insurers may want to reevaluate product designs to better manage the associated mortality risk. We then introduce a product idea with this in mind, where premiums are based on a life expectancy index and insurers share the risk (and opportunity) or mortality improvement with policyholders.

Description: This article provides actuaries with general information about the Oregon Death with Dignity Act and experience for pricing of life and other insurance products.

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: 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: This article examines the impact of a changing smoking rate on population-level mortality improvement and the importance of taking this driver into account.

Description: Originally featured as a breakout session at the 2015 CRC, this presentation discusses, from a North-American and non-actuarial perspective, how population growth and size have a major impact on socio-demographic needs in term of health care, social and old age security, education, transport and infrastructure. Their underlying components are two opposing forces, namely mortality and longevity. Understanding their past and possible future key drivers is not only important to the macro-economic planning of government agencies, but also to pension funds, insurance companies, and other stakeholders of the financial and insurance industries.

Description: This article details different methods for calculating mortality impact associated with new underwriting programs. A common simplified calculation is compared and contrasted with two first principles approaches. The strengths and weaknesses of both approaches are discussed.

Description: This article discusses the concept of Conservation of Mortality ("Conservation"). Conservation is the modeling principle that ensures consistency between assumptions of the total mortality of a population and mortality among population subsets. This article discusses the use cases for Conservation and the many ways that one could implement it in a model or an experience study. This article also discusses the potential issues that arise when using Conservation, and how one might solve or alleviate these issues.