Living benefit riders to life insurance policies (also known as ‘combo’ or ‘hybrid’ policies) have become a core component of life insurance sales strategy. LIMRA reported that in 2020 “Combination products represented 24 percent of life insurance sales based on total premium.” Concurrently, the long-term care insurance (LTCI) industry reached an inflection point when more LTCI (and chronic illness) benefits were sold through hybrid products than from standalone LTCI coverage.
On the spectrum of life and LTCI hybrid policies, the richest of these provide coverage of LTCI first through accelerating the policy’s death benefit, and then by providing extended LTCI benefits for many more years. There are a handful of individual and worksite insurers who sell these rich hybrid policies. On the other end of this spectrum are acceleration-only riders to life insurance policies. These riders provide policyholders the opportunity to receive a portion of the policy’s death benefit in advance, under certain conditions. Some of these riders do not cover qualified LTCI, but instead cover ‘chronic illness,’ which has a similar benefit trigger but is not formally LTCI.
This article outlines industry practice and consideration for pricing these acceleration-only policies. The National Association of Insurance Commissioners (NAIC) Model Regulation #620 addresses accelerated death benefit riders to life insurance policies. Model Regulation #620 outlines three financing methods for accelerated death benefit riders which we describe in this article. The Interstate Insurance Product Regulation Commission (the IIPRC, or the “Compact”) adopted standards for some of these riders in the Additional Standards for Accelerated Death Benefits (IIPRC-L-08-LB-I-AD-3). For companies filing chronic illness, critical illness, and terminal illness products in the Compact, these standards define—among other items—the form and actuarial submission requirements and benefit design options for accelerated death benefit riders. If a company is filing an acceleration rider for a qualified LTCI benefit, that product would be subject to the IIPRC individual LTC insurance standards.
Different mortality projection methodologies are utilized by actuaries across applications and practice areas. As a result, the SOA’s Longevity Advisory Group (“Advisory Group”) developed a single framework to serve as a consistent base for practitioners in projecting mortality improvement. The Mortality Improvement Model, MIM-2021-v2, Tools and User Guides, compose the consistent approach and are defined below.
A report describing MIM-2021-v2 which summarizes the evolution of MIM-2021-v2; provides an overview of MIM-2021-v2; presents considerations for applying mortality assumptions in the model; and outlines issues the Advisory Group is currently considering for future model enhancements.
A status report of the items listed in Section V of Developing a Consistent Framework for Mortality Improvement. This report advises practitioners about subsequent research and analysis conducted by the Advisory Group regarding these items.
An Excel-based tool, MIM-2021-v2 Application Tool, and user guide, MIM-2021-v2 Application Tool User Guide, for practitioners to construct sets of mortality improvement rates under this framework for specific applications.
An Excel-based tool, MIM-2021-v2 Data Analysis Tool, and user guide, MIM-2021-v2 Data Analysis Tool User Guide, for practitioners to analyze the historical data sets included in the MIM-2021-v2 Application Tool.
The Longevity Advisory Group is planning to update the framework annually as new data and enhancements become available. MIM-2021-v2 is the first revision since the initial release in April 2021. This version uses the same underpinning as the initial MIM-2021 release but has been refreshed to include another year of historical U.S. population mortality data as well as more user flexibility and functionality to replicate RPEC’s MP-2021 and O2-2021 scales.
The number of candidates sitting for entry level exam P and exam FM decreased over the last decade. Figure 1 below shows the total attempts for Exams P and FM halving over the past decade.
This represents an average decline of 7% per year across the two exams. This shows a major change from 2013 when the Actuarial Profession was consistently ranked #1 in national job lists and the number of candidates sitting for exams was growing year over year. For reference, Actuary is currently ranked #20, behind software developer (#5) and data scientist (#6).
One hypothesis is that data scientists and similar job openings are drawing potential actuaries away from the profession. To investigate this question, we queried fifteen colleges, actuarial clubs, and their recent graduates to see if this trend was noticeable, with key learnings summarized below:
Candidates at schools with Society of Actuaries (SOA)’s Centers of Actuarial Excellence (CAE) recognition are more than twice as likely to remain on the actuarial career path. Further, the strongest programs appear to attract other majors due to the top-tier program and resources
Recently established data science majors are pulling some students away from actuarial science and quite a few interviewees perceived that the popularity of the actuarial science program is declining
For international students, there is a general perception that it is harder to get an actuarial job that provides working visa sponsorship, while most data science jobs still provide sponsorship
The mixed results between the first two findings suggest that the strongest college actuarial programs are becoming stronger while schools with fledgling or small programs may be struggling. For example, actuarial career fairs tend to be successful only after achieving a level of scale so that they are well attended by both prospective hires and recruiters.
Decentralized finance, or DeFi, is an emerging financial system powered by blockchain technology. This research report aims to introduce actuaries to DeFi and help them develop a solid understanding of DeFi. It will begin with addressing “what is DeFi?” by providing an introduction on blockchains and DeFi. It will then discuss in further detail the key characteristics, applications, opportunities, and risks of DeFi. After providing the foundation, this report will discuss the potential adoption of DeFi and its interaction with the current financial system (sometime referred to as traditional finance for contrast with DeFi), and the implications for practicing and aspiring actuaries. In addition, a glossary of terms used in DeFi and a brief history of the development of DeFi have been included in the appendix.
LIMRA, Reinsurance Group of America (RGA), the Society of Actuaries (SOA) Research Institute, and TAI have collaborated on an ongoing effort to analyze the impact of COVID-19 on the individual life insurance industry’s mortality experience and share the emerging results with the insurance industry and the public. The Individual Life COVID-19 Project Work Group (Work Group) was formed as a collaboration of LIMRA, RGA, the SOA Research Institute, and TAI to design, implement, and create the study and to produce and distribute a variety of analyses. This report is the fifth public release from this collaboration and contains the results of the study of excess mortality for individual life insurance to include the second quarter of 2021. Data from 31 companies representing approximately 72% of the industry face amount in force have been included in the analysis in this report. A total of 3.0 million death claims from individual life policies from 2015 through June 30, 2021 make up the basis of the analysis.
Highlights for the 2nd Quarter
The second quarter of 2021 showed a significant realignment of the actual to expected relative mortality ratios, across many different cuts of the data.
It is worth noting that the third quarter 2021 results will likely not be as favorable due to the impact of the COVID-19 Delta variant whose impact first started in July 2021 and peaked around mid- September
All age groups improved in the second quarter compared to the first quarter of 2021, but the improvement was more dramatic in the older ages. While the three age groups shown under age 65 were still significantly over the trend established by 2015-2019, the age 65-84 group was within the 95% confidence bands and the age 85+ group was significantly better than the 2015-2019 trend (p < 0.05).
Whereas the pandemic experience so far had showed substantial variations across different regions, this appears to have moderated during the 2nd quarter of 2022.
Author(s): Individual Life COVID-19 Project Work Group, SOA
LIMRA, Reinsurance Group of America (RGA), the Society of Actuaries Research Institute (SOA), and TAI have collaborated on an ongoing effort to analyze the impact of COVID-19 on the individual life insurance industry’s mortality experience and share the emerging results with the insurance industry and the public. This report documents a high-level analysis of the claims that have been reported through December 31, 2021. The results presented here are based on data from 32 companies representing approximately 72% of the individual life insurance in force for the experience period of the study.
Author(s): Individual Life COVID-19 Project Work Group
Second, one of the key drivers of these stable and low benefit ratios has been steady-to-declining rates of claims incidence. In a recent paper published by the SOA and co‑authored by Gen Re’s Jay Barriss, Individual Disability incidence rates were shown to have steadily improved over the 2005 to 2015 period, relative to the latest Individual Disability Valuation Table (IDIVT) incidence rate expectations.10 The favorable incidence rate trends have likely continued into at least into 2020 as Gen Re analysis on our reinsured blocks of disability business show continuing-to-stable incidence trends since 2015.
Growing popularity in no-medical-exam life insurance products has had one expected outcome: More life insurance policies with accelerated underwriting options available in the marketplace. For example, Policygenius offered just three accelerated underwriting options in 2020. In 2021, that number more than doubled to seven, and more options will likely be available in 2022.
Additionally, while such policies had historically only been available to applicants who were young and in good health, the competitive market has prompted more widespread availability. Now, applicants across all health classes can get no-medical-exam policies.
While no-medical-exam policies tend to be about the same cost as fully underwritten policies, applicants tend to favor them even when they are more expensive due to the convenience and expedited turnaround time.
“The losses we are seeing continue to be elevated over 2019 levels due at least in part, we believe, to the pandemic and the existence of either delayed or unavailable healthcare,” Globe Life finance chief Frank Svoboda told analysts and investors earlier this month.
Among the non-coronavirus-specific claims are deaths from heart and circulatory issues and neurological disorders, he said. “We anticipate that they’ll start to be less impactful over the course of 2022 but we do anticipate that we’ll still at least see some elevated levels throughout the year,” he said.
Primerica executives similarly cautioned in their fourth-quarter call about outsize numbers of non-Covid-19 deaths in 2022. “Some of these will be the result of delayed medical care or the increased incidence of societal-related issues, such as the increased prevalence of substance abuse,” Chief Financial Officer Alison Rand said in an email interview.
From early stages of the pandemic, many medical professionals have raised concerns about Americans’ untreated health problems, as Covid-19 put stress on the nation’s healthcare system.
LDI is a key investment approach adopted by insurance companies and defined benefit (DB) pension funds. However, the complex structure of the liability portfolio and the volatile nature of capital markets make strategic asset allocation very challenging. On one hand, the optimization of a dynamic asset allocation strategy is difficult to achieve with dynamic programming, whose assumption as to liability evolution is often too simplified. On the other hand, using a grid-searching approach to find the best asset allocation or path to such an allocation is too computationally intensive, even if one restricts the choices to just a few asset classes.
Artificial intelligence is a promising approach for addressing these challenges. Using deep learning models and reinforcement learning (RL) to construct a framework for learning the optimal dynamic strategic asset allocation plan for LDI, one can design a stochastic experimental framework of the economic system as shown in Figure 1. In this framework, the program can identify appropriate strategy candidates by testing varying asset allocation strategies over time.
Some ML algorithms (e.g., random forests) work very nicely with missing data. No data cleaning is required when using these algorithms. In addition to not breaking down amid missing data, these algorithms use the fact of “missingness” as a feature to predict with. This compensates for when the missing points are not randomly missing.
Or, rather than dodge the problem, although that might be the best approach, you can impute the missing values and work from there. Here, very simple ML algorithms that look for the nearest data point (K-Nearest Neighbors) and infer its value work well. Simplicity here can be optimal because the modeling in data cleaning should not be mixed with the modeling in forecasting.
There are also remedies for missing data in time series. The challenge of time series data is that relationships exist, not just between variables, but between variables and their preceding states. And, from the point of view of a historical data point, relationships exist with the future states of the variables.
For the sake of predicting missing values, a data set can be augmented by including lagged values and negative-lagged values (i.e., future values). This, now-wider, augmented data set will have correlated predictors. The regularization trick can be used to forecast missing points with the available data. And, a strategy of repeatedly sampling, forecasting, and then averaging the forecasts can be used. Or, a similar turnkey approach is to use principal component analysis (PCA) following a similar strategy where a meta-algorithm will repeatedly impute, project, and refit until the imputed points stop changing. This is easier said than done, but it is doable.