The SOA Research Institute’s Mortality and Longevity Strategic Research Program is pleased to make available a research report that quantifies differences in mortality and disease prevalence by health status. Additionally, period life tables by health status, sex, and age are available in Appendix D.
Author(s):
Natalia S. Gavrilova, Ph.D. Leonid A. Gavrilov, Ph.D.
Demographers and actuaries make the following distinction between life expectancy and longevity: Life expectancy refers to the average number of years someone will live from a given age, whereas longevity refers to how long he or she might live if everything goes well, typically expressed as the probability of living beyond a certain age such as 85, 90 or even 100.
A growing body of evidence shows that many people are ignorant of their so-called longevity risk—the probability of living a very long time—and the complications that presents.
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Drs. Hurwitz and Mitchell note that retirement calculators provide information about average life expectancy, but not longevity. They have found that about five times as many Census Bureau publications relate to life expectancy as longevity. Thus, people who have planned appropriately for their life expectancy might miss how likely they are to live longer.
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People can look up their longevity risk with an online Longevity Illustrator maintained by the American Academy of Actuaries and Society of Actuaries, based off the latest mortality data from the Social Security Administration.
The Committee on Life Insurance Mortality and Underwriting Surveys of the Society of Actuaries sent companies a survey in May of 2019 on mortality improvement practices as of year-end 2018. The survey results were released in January 2022. The survey was completed by respondents prior to the onset of COVID-19. The present report provides an opportunity to update the results for pandemic-based changes and compare the before and after surveys. The 2022 survey was opened in March 2022 and closed by the end of April. Thirty-five respondent companies participated in this survey, with 29 from the U.S. and six from Canada. This group was further divided between direct writers (26) and reinsurers (nine). This survey focused on the use of mortality improvement and how it has changed for financial projection and pricing modeling following the initial stages of COVID-19. Details regarding assumptions and opinions on mortality improvement in general were asked of the respondents. National Association of Insurance Commissioners discussions on mortality improvement factors due to COVID-19 for reserving purposes have taken place, but this survey was conducted before any adjustments reacting to them. Seventy-four percent (26 of 35) of respondents indicated using durational mortality improvement assumptions in their life and annuity pricing and/or financial projections. Moreover, of those that used durational mortality improvement assumptions, attained age and gender were the top two characteristics in which assumptions varied. Respondents were asked to indicate the different limitations when applying durational mortality improvement assumptions. The Survey found that the most common lowest and highest attained age to which durational mortality improvement was applied were 0 and about 100, respectively. The lowest and highest durational mortality improvement rate ranged from -1.50% (deterioration) to 2.80% (improvement). The time period in which the mortality improvement rates were applied ranged from 10 to 120 years, but this varied between life (10/120) and annuities (30/120). The most common time period was 20 to 30 years for life; less consensus was seen for annuities. Analysis is provided in Appendix C for instances when highlights are shared in the body of the report.
Higher interest rates already have translated into higher discount rates for solvency and accounting valuations, which means good news (lower liabilities) for DB pension plans. The sensitivity of a pension plan’s liabilities to the discount rate used to determine their value depends on the demographics of the plan members, the type of valuation and level of discount rates being used. Generally, the “duration” for most pension plan liabilities (defined here as the percentage decrease in liabilities for a 1% increase in discount rates) will range from 10 to 25.
In the United States, the average accounting funded ratio increased from 94.6% in July 2021 to 104.5% in July 2022, according to the Milliman 100 Pension Funding Index, despite significant decreases in plan assets during that time. This is because the average accounting discount rate (typically based on long-term, high-quality bond yields) increased from 2.59% to 4.25% during that same period, driving down accounting liabilities at a faster pace than asset losses. Figure 1 demonstrates this effect in more detail.
CHAPTER HIGHLIGHTS: • Despite the COVID-19 pandemic, level of concern about various risks remains historically low this year for both pre-retirees and retirees. Compared to 2019, level of concern dropped on some issues for retirees. As a result of this drop, retiree concerns are lower than those of pre-retirees by a larger gap than ever before. • The one exception to this trend was concern about fraud. In 2021, both retirees and pre-retirees were more concerned about fraud, and it is the highest concern among retirees, particularly Black/African American retirees. As in prior studies, those with lower income tend to show much higher levels of concern. • The biggest concerns for pre-retirees are their savings and investments not keeping up with inflation, not being able to afford long-term care, not being able to afford health care costs, not being able to maintain a reasonable standard of living throughout retirement, and potentially depleting all their savings. • While half of pre-retirees plan to retire gradually rather than all at once, retiree respondents indicate this seldom actually happens. Higher-income pre-retirees are more likely to plan to go straight from full time employment to retirement. • The COVID-19 pandemic has not affected plans that pre-retirees have for work, living arrangements, and lifestyle in retirement, although over a quarter report changing their lifestyle. • Despite the financial challenges that retirement poses, most do not have financial advisors, especially preretirees, lower-income respondents, and Black/African American respondents.
The Society of Actuaries (SOA) Research Institute released a report that examines older age mortality (OAM) with a focus on attained ages 70 and above. The report helps determine whether refinements were needed in the 2015 Valuation Basic Tables. Analysis was performed by sex, issue age and attained age, issue year cohorts, smoking risk classification, benefit band, select vs ultimate period, and interactions.
Author(s):
Old Age Mortality Subgroup of the Individual Life Experience Committee
The DeFi ecosystem has been expanding rapidly in the past few years, growing from less than USD $1 billion in 2020 to USD $61.6 billion as of June 2022 as measured by Total Value Locked (TVL), the amount of crypto asset deposited in the DeFi protocols.
With continuous innovation in product design and delivery, the potential of DeFi adoption is massive. However, the rise of DeFi is marred by security issues. Nearly 200 blockchain hacking incidents have taken place in 2021 with approximately USD $7 billion in stolen funds (Cointelegraph, 2021). These hacking events have a wide range of causes including, but not limited to, the following:
Smart contract vulnerabilities exploited by hackers to steal funds
Manipulation of oracles to cause price feed deviation
Attack on governance where a small group of individuals took over the protocol’s governance decisionmaking mechanism
Decentralized finance (DeFi) is an emerging and rapidly growing financial ecosystem with the defining feature that it is powered by blockchain technology. The focus of this paper is on risks for DeFi protocols that could lead to economic losses that could be insurable. This framework was designed around the risks associated with the existing and emerging DeFi protocols.
The Society of Actuaries (SOA) leadership and staff work closely with the Diversity, Equity, and Inclusion Committee (DEIC) to support the journey to increase diversity in membership and in the actuarial profession, as part of the SOA’s Long-Term Growth Strategy.
We strive for transparency and accountability in our DEI efforts and are committed to sharing our demographic data and long-term goals to support our pledge and responsibility. We have collected member voluntary demographic data since 2015. With this data, we present an infographic for the pathway from aspiring actuaries to members with ASA or FSA designations.
Before we get into the different approaches, why should you care about knowing multiple ways to calculate a distribution when we have a perfectly good symbolic formula that tells us the probability exactly?
As we shall soon see, having that formula gives us the illusion that we have the “exact” answer. We actually have to calculate the elements within. If you try calculating the binomial coefficients up front, you will notice they get very large, just as those powers of q get very small. In a system using floating point arithmetic, as Excel does, we may run into trouble with either underflow or overflow. Obviously, I picked a situation that would create just such troubles, by picking a somewhat large number of people and a somewhat low probability of death.
I am making no assumptions as to the specific use of the full distribution being made. It may be that one is attempting to calculate Value at Risk or Conditional Tail Expectation values. It may be that one is constructing stress scenarios. Most of the places where the following approximations fail are areas that are not necessarily of concern to actuaries, in general. In the following I will look at how each approximation behaves, and why one might choose that approach compared to others.
Under the old regime, the impairment was the incurred credit losses, in determining which only past events and current conditions are used. Credit losses were booked after a credit event had taken place, thus the name “incurred.” ECL and CECL require the incorporation of forward-looking information in addition to the past/current info in the calculation of impairment. There will be an allowance for credit losses since initial recognition regardless of the creditworthiness of the investment asset. The allowance can be perceived as the reserve or capital for credit risks. In practice, the allowance could be zero if there are no expected default losses for the instrument, US Treasury bonds, US Agency MBS, just to name a few.
ECL under IFRS 9 is typically calculated as a probability weighted estimate of the present value of cash shortfalls over the expected life of the financial instrument. It Is an unbiased best estimate with all cash shortfalls taking into consideration the collaterals or other credit enhancement. Four typical parameters underlying its calculation are: Probability of default (PD), loss given default (LGD, i.e., 1-Recovery Rate), exposure at default (EAD) and discounting factor (DF). Prepayments, usage given default (UGD) and other parameters can also play a role in the calculations. In the general approach the loss allowance for a financial instrument is 12-month ECL regardless of credit risk at the reporting date, unless there has been a significant increase in credit risk since initial recognition: The PD is only considered for the next 12 months while the cash shortfalls are predicted over the full lifetime; as the creditworthiness deteriorates significantly, the loss allowance is increased to full lifetime ECL in Stage 2, which should always precede stage 3 (credit impairment). Even without change of stages, any credit condition changes should be flowing into the credit loss allowance via updates in some of the underlying parameters. Exhibit 1 has an illustrative comparison between ECL, CECL, and incurred loss model.
CECL is similar to ECL except FASBs doesn’t have so-called staging as IFRS 9, which requires that only 12-month ECL is calculated in stage 1 (in the general model). In other words, CECL requires a full lifetime ECL from Day 1. There are also other differences: IFRS 9 requires certain consideration of time value of money, multiple scenarios, etc., in measurement of ECL while US GAAP CECL doesn’t.
Under US GAAP, different from CECL, currently the impairment for AFS assets, while also recorded as an allowance (with a couple exceptions), is only needed for those whose fair value is less than the amortized cost. Once it is triggered, the credit losses are then measured as the excess of the amortized cost basis over the probability weighted estimate of the present value of cash flows expected to be collected. Only the fair value change related to credit is considered in the calculation of AFS impairment. The quantitative calculation behind the probability weighted best estimate is like CECL/ECL. Both can use discounted cash flow methods with parameters such as PD although one is calculating expected cash shortfalls directly in CECL and the other is calculating the expected collectible cash payments and then is used to back out the impairment.
Life technical risks measure the possible losses from deviations from the best estimate assumptions relating to life expectancy, policyholder behavior, and expenses. The life technical risks are captured through mortality, longevity, morbidity, and other risks. The methodology for calculating the capital adequacy for these four risk categories remains unchanged under the proposed method, apart from the recalibration of capital charges or the consolidation of defining categories within each risk. Comparing to the current GAAP based model, charges have materially increased across all categories partly due to higher confidence intervals, with notable exceptions of longevity risk, with reduced charges across all stress levels (changes applicable to U.S. life insurers are illustrated in Tables A2 to A5 in the Appendix linked at the end of this article). Please note that S&P’s current capital model under U.S. statutory basis does not have an explicit longevity risk charge. However, this article focuses on comparison to current GAAP capital model[1] that is closer to the new capital methodology framework.
For mortality risk, lower rates are charged for smaller exposures (net amount at risk (NAR) $5 billion or less) with the consolidation of size categories, but higher rates are charged for NAR between $5 billion and $250 billion, with an average increase of 49 percent for businesses under $400 billion NAR. A new pandemic risk charge (Table A3 in the Appendix linked at the end of this article) will further increase mortality related risk charges to be 109 percent higher than original mortality charges under confidence level for company rating of AA, and 93 percent higher for confidence level for company rating of A, respectively, on average (Figure 1). The disability risk charge rates increased moderately for most products, across all eight product types such that the increase of disability premium risk charges is 6 percent under confidence level for AA, and 2 percent for A, respectively. In addition, the proposed model introduced a new charge on disability claims reserve, ranging from 13.7 percent of total disability claims reserves for AAA, to 9.6 percent for BBB. However, the proposed model provides lower capital charge rates in longevity risk and lapse risk.
Author(s): Yiru (Eve) Sun, John Choi, and Seong-Weon Park
Publication Date: September 2022
Publication Site: Financial Reporting newsletter of the SOA
