Economic rates throughout the world have risen sharply in recent years. When considering both the magnitude and the persistency of the higher rates, we find ourselves in an economic environment unlike any experienced in decades.
This article will explore the impacts of the increase in rates on the U.S. life insurance industry and review some of the key issues and risks faced. Additionally, this article will review some common actions many U.S. life insurers are performing to assess and manage these issues and risks.
Note: The article includes a short three-question survey in the right margin, and the results will be shared with all participants.
The 2008 mortgage-backed securities (MBS) crisis saw a coordinated response by central banks worldwide and led to historically low rates. Central bank rates and government lending rates worldwide reached 0% and, in some instances, even went negative. However, this long period of low rates was also a continuation of a decades-long trend of rates dropping. In the U.S., rates reached historical highs in the early 1980s. Since then, they have been on a long-term downward trend. After the long period of historically low rates precipitated by the MBS financial crisis, rates have recently risen significantly in the U.S. and across the world.
Figure 1 focuses on the 10-year U.S. Treasury par rate over this extended period. The graph demonstrates the long-term trend, with a maximum value of 15.32% in the early 1980s and a low value just above 0.5% (0.62%) in 2020, before the 10-year U.S. Treasury began rising.
U.S. life insurance products are fundamentally based on a promise to pay future values using funds provided either all up front or over the life of the policy. The funds provided are largely invested in fixed-income portfolios.
Many products incorporate an accumulation component to the value of the insurance. Some common examples are many deferred annuities and some life products:
In all cases, the foundation of the assets to support these products are fixed-income portfolios. A common practice for the investment portfolios is to use an asset liability management (ALM) approach, which aims to match durations.
The rate at which policyholders elect to leave their policies (or surrender) has been shown to be influenced by the state of the economy. As such, most companies incorporate a dynamic surrender function into their actuarial projection assumptions.
The basic principle of a dynamic surrender involves comparing the value to a policyholder from staying in his or her current policy to the value he or she would be able to receive from surrendering the policy and purchasing a new policy. Numerous methods are available for estimating the value that can be obtained from a new policy. Most are fundamentally built around comparing a current key crediting rate in an existing policy, where the key rate depends on the product type (e.g., crediting rate for a MYGA, option budget for an FIA) to an estimated potential similar rate that could be received for a new policy. These are often referred to as the current rate (CR) and the market rate (MR). When the MR is lower than the policy’s CR, surrenders are reduced, and conversely when the MR is higher than the CR, surrenders are increased. Insurers have often used historical data to calibrate the parameters of their dynamic surrender functions to estimate their expectations of the amount of decrease or increase. From Figure 1, we see that historical data would overall be from a period where the trend for rates had been dropping.
When estimating the MR, while numerous practices are utilized, it is very common to base the estimate on a single tenor UST plus a long-term average assumption for credit spreads, which can be achieved with new money investments. This sum may then be decreased by an average for an assumed pricing spread that competitors will try to achieve. Said another way, a typical approach to estimating MR might be based on something like:
MR = n Year UST Rate + Assumed Credit Spread – Assumed Target Pricing Spread
Note: Some companies use just MR = n Year UST Rate + a spread, and the spread used reflects the net of the credit spread and the target pricing spread, so it is the same concept.
Of course, there is a wide variation in the products sold, with varying surrender charge periods and varying liability durations. The tenor of the UST used can and will vary depending on the product and the companies’ views of their competitors, as well as the actuarial model they use. That being said, U.S. life insurers almost always incorporate a large amount of their investment portfolios with assets with tenors of five, seven and 10 years. It is therefore common for U.S. insurers to use either a five-year or 10-year UST rate to represent the “n” in their MR calculation.
In this article, while we will continue to focus on the 10-year UST, the issues we highlight will apply to other tenor UST rates as well.
To further highlight how the 10-year UST rate has changed over time, Figure 2 shows the difference in the 10-year rate on a given date to what it was three years in the past and five years in the past. The difference in the key rates (five, seven or 10) in these time periods (three or five years ago) is of particular interest to U.S. insurers to show how policies sold three and five years ago are likely to behave. It is particularly relevant as products with significant fixed-income asset portfolios will often have surrender charge periods of five, seven or 10 years. Historical data have shown that policyholders tend to be more dynamic in their behavior when penalties for doing so are zero (or close to it).
Figure 2 shows the difference of the 10-year UST rates to values three or five years prior (the two dotted lines, with the difference value shown on the right-hand y-axis).
From this graph, we can see that compared with rates three and five years ago, the differences in 10-year UST rates are currently greater than they have been at any time in the past 40 years. This is important when considering the calibration of the dynamic surrenders using historical policyholder behavior. Prior to late 2022, policies have not been in the condition where market rates are as significantly above what they were when the policy was purchased. Figure 2 shows that for policies purchased three to five years ago, current market rates are approximately 1.5% to 3.5% greater than at policy issue. In fact, as shown by the arrow in the graph, the current 10-year UST is greater than it has been for well over 10 years.
As we have discussed, companies calibrate their dynamic surrender formulas based on historical behavior. Additionally, as we have seen in figures 1 and 2, the current macroeconomic environment has led to a condition that has not been seen at any point in recent history. Given this, any experience study data would be extremely limited in its ability to support calibrating a dynamic surrender formula. Parameters used to set the results of the dynamic surrender in such an increasing rate environment have been informed estimations (guessing) instead of based on actual results. Many companies are now questioning their estimates for policyholder behavior when the MR is significantly above the CR.
The recent liquidity issues in the banking industry, highlighted by the collapse of Silicon Valley Bank, have served to further exacerbate the concern many in the U.S. life insurance industry have around their current projection methods and dynamic assumptions.
Of course, the liability impacts cannot be thought about in isolation, as ALM practices involve using estimated liability cash flows to develop and manage asset investments. Further, best practice risk management and inforce management are predicated on accurately estimating future liability cash flows. As such, best practices often incorporate monitoring of policyholder behavior as well as rigorous actual-to-expected analysis of the results. Based on this analysis, numerous U.S. life insurers have shared with WTW that they are seeing results for surrenders below what their current dynamic surrenders are projecting. In addition, many have shared that they are revisiting this assumption, and we expect that many will also be performing updated experience studies, perhaps timed to support the common assumption unlocking period of the third quarter in 2023.
When considering updated experience studies, there are some key considerations:
It is difficult to predict how market rates will behave in the future. UST rates may continue to stay elevated or potentially rise further to combat inflation, or they may drop back down to support an economy that enters a recession. Despite this, using the recently evolving unique experience, the insurance industry now has an opportunity to calibrate its dynamic behavior assumptions better. Many companies are revisiting the calibration of their dynamic behavior functions. Accurate calibration of this dynamic behavior function has some key challenges, such as:
It is worth considering if it may be best to limit any changes made to the current assumption parameters. It will be better to incorporate a partial move in the direction of recent behavior so they do not over-correct the dynamic parameters instead of changing them to fully match the recent behavior only to change them again in the opposite direction in the near future. In all instances, we expect companies will monitor this behavior very closely for the foreseeable future.