Tag: Contingencies

IFRS 17—The Time Is Now

Mary Pat Campbell

Link: https://contingencies.org/ifrs-17-the-time-is-now/

Excerpt:

The more fundamental changes affect the measurement of future services (previously termed as “Reserves”). Many insurance accounting regimes have tried to stabilize their financial statements over the years; therefore, they calculated their reserves based on historic information—locked-in assumptions for insurance parameters as well as historic interest rates. The latter, however, are not in line with the use of market values for the asset side of the balance sheet, which is now perceived as the only fair-value representation for the different stakeholders. Therefore, the measurement of the liabilities in IFRS 17 will always be based on current assumptions.

Due to the compound effect over many projected years, the regular update of assumptions (particularly interest rate or discounting assumptions) can make long-term liabilities much more volatile.

Author(s): Michael Winkler and Sunil Kansal

Publication Date: October 2022

Publication Site: Contingencies

What happens when the public health emergency associated with COVID-19 ends?

Mary Pat Campbell

Link: https://contingencies.org/the-great-unwinding/

Excerpt:

The ongoing COVID-19 pandemic has now spanned three years. A lot has changed and will continue to change once society and every industry, especially health care, adjusts to the new post-COVID world. With the pandemic, a federal public health emergency (PHE) was declared, and legislation was then passed that had a major impact on how health care is administered from both an operational and financial perspective. Many temporary provisions were put into place that mostly impact Medicaid but ultimately affect all health insurance payers. As we look ahead to a point at which the PHE ends, those temporary provisions start to end in what many in the industry are calling the “unwinding of the PHE.” This article aims to provide an overview of the flexibilities that have been offered as a result of legislation tied to the PHE, examine the impacts of increased Medicaid enrollment, and assess how the risk profile of covered lives for all health insurance payers has changed.

The PHE that has been in effect because of the virus SARS-CoV-2 (which causes the disease COVID-19, or simply COVID), was declared on March 12, 2020, retroactively effective as of Jan. 31, 2020. 

….

Where does this leave us now? At the time of this writing, the PHE is under its ninth renewal (90-day extensions) and is set to expire July 15, 2022. HHS has previously informed states that at least 60 days’ notice will be provided, which means the end of the PHE will occur July 2022 or later. States receive the additional FMAP bump through the end of the quarter in which the PHE ends, which is slated to be Sept. 30, 2022. Before the omicron wave, many thought the PHE would end in early 2022. Popular opinion seems to have shifted to a later time period, with mid-to-late 2022 being the likely end of the PHE. Any continued uncertainty with the pandemic, such as another wave of cases, is likely to extend the PHE.

As we get close to the end of the PHE though, the focus shifts from case counts and test kits to the virus becoming endemic and moving past the PHE. This puts, front and center, the unwinding of all of the operational and financial elements that have been tied to the PHE since FFCRA was passed. When the unwinding starts, it will radically change the risk profile of Medicaid and all other health payors. Measuring and mitigating against this changing risk profile is where the nature of our profession as actuaries becomes critical. The biggest driver in the changing risk profile is the enrollment growth that has occurred with Medicaid since the pandemic began, as a number of these new members are at risk of losing their coverage.

Author(s): Colby Schaeffer

Publication Date: May/June 2022

Publication Site: Contingencies

Who Cares About Life Expectancy?

Mary Pat Campbell

Link:https://contingencies.org/who-cares-about-life-expectancy/

Excerpt:

Life expectancy at birth (LEB) in the U.S. has grown about 50% since 1900, with most of the increase going to upper income groups. (See “Differences in Life Expectancy by Income Level”; Contingencies;July/August 2016.)Depending on the data source and the methodology used to determine it, LEB in the U.S. is about 77 and 82 for males and females, respectively.

I’m a retiree, so I’m more interested in life expectancy at age 65 (LE65). (OK, fine, life expectancy at a somewhat higher age is more pertinent for me, but LE65 is the more common measurement.) LE65 in America is about 18.2 and 20.8 for males and females, again depending on the dataset and methodology.

LEB and LE65 in America are calculated from a dataset of 330 million lives. Another dataset of 7.5 billion lives provides a LEB of 68 and 72 for males and females, a significant difference from the LEB mentioned earlier. The 7.5-billion-life dataset was the world population rather than the U.S. population subset. A meaningful LEB requires homogeneity of the underlying dataset.

Author(s): Bob Rietz

Publication Date: Jan/Feb 2022

Publication Site: Contingencies

USQS Roundtable—All About the Amended Standards

Mary Pat Campbell

Link:https://contingencies.org/usqs-roundtable-all-about-the-amended-standards/

Excerpt:

TC: The work of the actuary is evolving more and more toward big data and artificial intelligence. In addition, we are seeing evolving regulatory and societal requirements that will place new demands on the actuary’s work. These new areas involve working with more unknowns in the tools actuaries use—such as data, models, algorithms, and assumptions. In order to be effective in these new areas, and to continue to earn the public’s trust in our work, we need to better understand what can impact the appropriateness and effectiveness of these tools. As these areas evolve, it is important for actuaries to understand the potential limits of these tools. This is where obtaining continuing education on bias topics can help. As the USQS lay out, bias topics may include “content that provides knowledge and perspective that assist in identifying and assessing biases that may exist in data, assumptions, algorithms, and models that impact Actuarial Services. Biases may include but are not limited to statistical, cognitive, and social biases.” This is a broad topic, but I believe it will better equip the actuary in our role of maintaining the public’s trust in insurance and pension systems.

LS: Indeed, bias topics are broad. When performing actuarial services there are so many ways that bias impacts our work that we need to keep the topic broad in order that the range of continuing education will give us the appropriate tools. The obvious ways that bias may impact our work are in selection of data, as well as designing, developing, selecting, modifying, or using all types of models and algorithms. Even more important is how we communicate the results of our work. We also operate in a world where we can individually be blindsided by biases that we bring to our work and impact the transparency and validity of the actuarial services that we are providing. Because of our basic education, we know what bias is. That is something that we can continue to fine-tune and will have significant benefits to the reputation of actuaries and allow us to further differentiate our professionalism compared with others, particularly many data scientists.

Publication Date: Jan/Feb 2022

Publication Site: Contingencies

Applying Predictive Analytics for Insurance Assumptions—Setting Practical Lessons

Mary Pat Campbell
Applying Predictive Analytics for Insurance Assumptions—Setting Practical Lessons

Graphic:

Excerpt:

3. Identify pockets of good and poor model performance. Even if you can’t fix it, you can use this info in future UW decisions. I really like one- and two-dimensional views (e.g., age x pension amount) and performance across 50 or 100 largest plans—this is the precision level at which plans are actually quoted. (See Figure 3.)

What size of unexplained A/E residual is satisfactory at pricing segment level? How often will it occur in your future pricing universe? For example, 1-2% residual is probably OK. Ten to 20% in a popular segment likely indicates you have a model specification issue to explore.

Positive residuals mean that actual mortality data is higher than the model predicts (A>E). If the model is used for pricing this case, longevity pricing will be lower than if you had just followed the data, leading to a possible risk of not being competitive. Negative residuals mean A<E, predicted mortality being too high versus historical data, and a possible risk of price being too low.

Author(s): Lenny Shteyman, MAAA, FSA, CFA

Publication Date: September/October 2021

Publication Site: Contingencies