Tag: SOA

Trends in Life Insurance 2022: How the Industry Has Changed

Mary Pat Campbell

Link: https://www.soa.org/sections/reinsurance/reinsurance-newsletter/2022/april/rsn-2022-04-gambhir/

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Excerpt:

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.

Author(s): Nupur Gambhir

Publication Date: April 2022

Publication Site: Reinsurance News, SOA

Rise in Non-Covid-19 Deaths Hits Life Insurers

Mary Pat Campbell

Link: https://www.wsj.com/articles/rise-in-non-covid-19-deaths-hits-life-insurers-11645576252

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In earnings calls for the past two quarters, Globe Life Inc.,Hartford Financial Services Group Inc., Primerica Inc. andReinsurance Group of America Inc. were among insurers noting higher non-Covid-19 deaths, compared with pre-pandemic baselines.

“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.

Author(s): Leslie Scism

Publication Date: 23 Feb 2022

Publication Site: WSJ

Deep Learning for Liability-Driven Investment

Mary Pat Campbell

Link: https://www.soa.org/sections/investment/investment-newsletter/2022/february/rr-2022-02-shang/

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This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which was sponsored by the Committee on Finance Research of the Society of Actuaries. The paper applies reinforcement learning and deep learning techniques to liability-driven investment (LDI). The full paper is available at https://www.soa.org/globalassets/assets/files/resources/research-report/2021/liability-driven-investment.pdf.

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.

Author(s): Kailan Shang

Publication Date: February 2022

Publication Site: Risks & Rewards, SOA

What Machine Learning Can Do for You

Mary Pat Campbell

Link: https://www.soa.org/sections/investment/investment-newsletter/2022/february/rr-2022-02-romoff/

Excerpt:

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.

Author(s): David Romoff

Publication Date: February 2022

Publication Site: Risks & Rewards, SOA

COVID-19 Is Increasing Individual Life Claims, Too: Analysis

Mary Pat Campbell

Link:https://www.thinkadvisor.com/2022/02/14/covid-19-is-increasing-individual-life-claims-too-analysis/

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The increase in the number of individual life claims was lower than for group life claims in some quarters but higher in others.

The analysts emphasize that the numbers are incomplete and subject to change.

Early results show that the number of claims was higher in the fall than in the summer both for individual life and group life.

Author(s): Allison Bell

Publication Date: 14 Feb 2022

Publication Site: Think Advisor

COVID Waves in 2020 Caused Bigger U.S Death Rate Spike Than 1918 Flu: Actuaries

Mary Pat Campbell

Link:https://www.thinkadvisor.com/2022/01/26/covid-waves-in-2020-caused-bigger-u-s-death-rate-spike-than-1918-flu-actuaries/

Excerpt:

The pandemic led to the biggest U.S. death rate increase from causes other than COVID-19 since 1936.

The death rate in the highest-income counties increased to 736.1 deaths per 100,000 people, from 638.4 per 100,000 in 2019

For people ages 5 through 44, increases in the death rate from causes other than COVID-19 were much bigger than the increase caused directly by COVID-19.

Author(s): Allison Bell

Publication Date: 26 Jan 2021

Publication Site: Think Advisor

Getting Started with Julia for Actuaries

Mary Pat Campbell

Link:https://www.soa.org/digital-publishing-platform/emerging-topics/getting-started-with-julia/

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Sensitivity testing is very common in actuarial workflows: essentially, it’s understanding the change in one variable in relation to another. In other words, the derivative!

Julia has unique capabilities where almost across the entire language and ecosystem, you can take the derivative of entire functions or scripts. For example, the following is real Julia code to automatically calculate the sensitivity of the ending account value with respect to the inputs:

When executing the code above, Julia isn’t just adding a small amount and calculating the finite difference. Differentiation is applied to entire programs through extensive use of basic derivatives and the chain rule. Automatic differentiation, has uses in optimization, machine learning, sensitivity testing, and risk analysis. You can read more about Julia’s autodiff ecosystem here.

Author(s): Alec Loudenback, FSA, MAAA; Dimitar Vanguelov

Publication Date: October 2021

Publication Site: SOA Digital, Emerging Topics

Electronic Health Records in the Age of Coronavirus: The Covid Crisis Has Accelerated Real-World Adoption

Mary Pat Campbell

Link:https://www.soa.org/sections/technology/technology-newsletter/2021/october/att-2021-10-timmins/

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As with most things in the world, early 2020 seems like ancient history. In late January last year, I had just finished a white paper for the SOA[1], stating confidently that Electronic Health Records, or EHRs, were not likely to have a major impact for several years amid slow adoption by executives—with significant strategic differences among stakeholders and little signs of compromise.

Then in February came COVID-19, and the urgent need to move rapidly to telemedicine for both COVID and non-COVID afflictions. Government authorities moved quickly to relax restrictions on interstate telehealth, allowing payer coverage for the transfer of sensitive material online and over mobile networks—including Facebook Messenger, Apple Facetime, and Zoom.[2] Although not permanent while a public health emergency, this created an unexpected precedent for interoperability, the Holy Grail of seamless cross-talk of health data between HIPAA-regulated IT systems.

….

Much remains unknown about the secondary effects of COVID-19, beyond respiratory failure and immune system overload. However, there is likely a significant spike in claims related to COVID-19 building within the insurance industry. It may be important for the insurance industry to monitor these secondary afflictions, at minimum through the claims process (see Figure 3), although the best digital approach to obtain that evolving health information remains unclear at this time. Genomics and epigenetic vulnerability could be a rich data area here.

If insureds would be willing (or mandated) to provide their immunization history to payors, directly or indirectly, this could be a significant asset for actuaries in evaluating this ongoing phenomenon. Claims data also can include non-prescription drug information, which could provide additional clues of COVID exposure.

Author(s): James Timmins

Publication Date: October 2021

Publication Site: Actuarial Technology Today

U.S. Population Mortality Observations – Updated with 2020 Experience

Mary Pat Campbell

Link:https://www.soa.org/resources/research-reports/2022/us-population-mortality/

pdf: https://www.soa.org/globalassets/assets/files/resources/research-report/2022/population-mortality-observation.pdf

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The overall age-adjusted mortality rate (both sexes) from all causes of death recorded the historically highest increase of published records dating back to 1900 of 16.8% in 2020, following a 1.2% decrease in 2019. The increase eclipsed the size of recent years’ annual volatility and exceeded the 11.7% increase in
1918 that occurred during the Spanish influenza pandemic. When COVID deaths are removed, all other
CODs’ (Cause of Death) combined mortality increased by 4.9%, which was last exceeded by a 5.6% increase in 1936.

All other CODs featured in this report had increased 2020 mortality. In many instances, the single year
mortality increases were the largest for the span of this report. Heart disease and Alzheimer’s/Dementia
had 4.7% and 7.8% increases, respectively. Other physiological CODs with lower death rates had double-digit increases. Diabetes, liver and hypertension had increases of 14.9%, 16.0% and 13.3%, respectively.
The external CODs of assaults and opioid overdoses had extreme increases at ages 15-24 of 35.9% and
61.2%, respectively.

Author(s):

Jerome Holman, FSA, MAAA, RJH Integrated Solutions, LLC
Cynthia S. MacDonald, FSA, MAAA, Society of Actuaries Research Institute

Publication Date: Jan 2022

Publication Site: SOA

Emerging Technologies and their Impact on Actuarial Science

Mary Pat Campbell

Link: https://www.soa.org/globalassets/assets/files/resources/research-report/2021/2021-emerging-technologies-report.pdf

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This research evaluates the current state and future outlook of emerging technologies on the actuarial profession
over a three-year horizon. For the purpose of this report, a technology is considered to be a practical application of
knowledge (as opposed to a specific vendor) and is considered emerging when the use of the particular technology
is not already widespread across the actuarial profession. This report looks to evaluate prospective tools that
actuaries can use across all aspects and domains of work spanning Life and Annuities, Health, P&C, and Pensions in
relation to insurance risk.
We researched and grouped similar technologies together for ease of reading and understanding. As a result, we
identified the six following technology groups:

  1. Machine Learning and Artificial Intelligence
  2. Business Intelligence Tools and Report Generators
  3. Extract-Transform-Load (ETL) / Data Integration and Low-Code Automation Platforms
  4. Collaboration and Connected Data
  5. Data Governance and Sharing
  6. Digital Process Discovery (Process Mining / Task Mining)

Author(s):

Nicole Cervi, Deloitte
Arthur da Silva, FSA, ACIA, Deloitte
Paul Downes, FIA, FCIA, Deloitte
Marwah Khalid, Deloitte
Chenyi Liu, Deloitte
Prakash Rajgopal, Deloitte
Jean-Yves Rioux, FSA, CERA, FCIA, Deloitte
Thomas Smith, Deloitte
Yvonne Zhang, FSA, FCIA, Deloitte

Publication Date: October 2021

Publication Site: Society of Actuaries, SOA Research Institute

Practical Application of “Do Jumps Matter in the Long Term? A Tale of Two Horizons”

Mary Pat Campbell

Link:https://www.soa.org/globalassets/assets/files/resources/naaj-practical-application-essays/2021/naaj-essay-cantor.pdf

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In this essay, we consider an additional application. Using the utility framework described by Warren (2019), we
examine the impact of using one of BB’s fitted jump-diffusion models on a pension plan sponsor’s long-term asset
allocation decision. We want to compare asset allocation results to those using the standard finance workhorse
model of a geometric Brownian motion (i.e., lognormal return generating process or LN hereafter).

Author(s):

Jean-François Bégin, PhD, FSA, FCIA
Mathieu Boudreault, PhD, FSA, FCIA
David R. Cantor, CFA, FRM, ASA
Kailan Shang, FSA, ACIA, CFA, PRM

Publication Date: September 2021

Publication Site: Society of Actuaries

“Ending Jim Crow Life Insurance Rates”: A Professionalism Case Study Webcast

Mary Pat Campbell

Link: https://www.soa.org/prof-dev/webcasts/2021-ending-jim-crow/

Description:

This session will introduce a late 19th century article by actuary FL Hoffman. This article provided a justification for racially discriminatory life insurance premiums – a practice that existed well into the 20th century and was consistent with “Jim Crow” thinking. Join the presenters as they discuss the reasons that Hoffman’s article was actuarially unsound using commentaries at the time it was written as well as recent publications that include important reflections on the actuarial profession that apply both historically and in the present. The session will discuss thoughts about how Hoffman’s work would be received and handled today, including the role of the ABCD in such matters, several rules and regulations that now guide actuarial conduct and are designed to prevent discriminatory practices, and actuarial ethical responsibilities. The session will conclude with an observation that one of the best ways for the actuarial profession to prevent the use of racially discriminatory practices in our work is by having a diverse actuarial profession with members who will provide first-hand perspectives about inappropriate actuarial practices.

Author(s):

Jay M. Jaffe, FSA, MAAA

Reinsurance Administration, Ltd.

Webcast Date (to come): 10 November 2021

Publication Site: SOA