A paradigm shift is a fundamental change in approach from all that has preceded and for which we may not be well prepared. Today’s 24-hour news cycle—enabled by advanced telecommunications, the Internet, and the ubiquitousness of “smart” hand-held devices—is an example of a paradigm shift that has dramatically altered how we receive information about the world around us, but does not necessarily alter what we do with that information.  Our attitudes and beliefs often lag the development of new ways of thinking or doing.

In the world of life insurance, carriers and agents pushed/pulled on a shift from guaranteed premiums, cash values and death benefits in the early 1980s to what became the vastly more popular current assumption / universal life-style of policies.  In the era of high interest rates in which UL was “born,” the industry spurned the higher but guaranteed whole life premiums that support lifetime death benefits. 

Instead, we sold flexibility and timing of payments, along with a naively high degree of comfort that those payments would suffice in spite of the fact that UL premiums are neither specified nor guaranteed, and that interest rate declines would inevitably adversely affect such policies.  These products clearly weren’t “…your father’s Oldsmobile,” and yet we continued to use such key words as premium as if it meant the same when acquiring pre-shift whole life policies.

An example of the dramatic evolution in life insurance product design (from the once considered guaranteed bedrock on which other financial planning concepts could be built) was the development of at-risk, segregated account polices and their illustrations.  Assumed 12 percent gross returns (the regulated maximum for variable life insurance illustrations) seemed reasonable in the look-back period of 1982 through early 2000 when the S&P500 yielded an average geometric return of more than 18 percent per year.

A 12 percent growth assumption resulted in a very low $3,617 projected planned premium (37-M-Preferred non-smoker), and was certainly more attractive than a whole life premium of more than $13,000.  But “planned premium” and “guaranteed premium” are not similar concepts.  The lower “premium” looked good on paper, but our appreciation for the underlying risks did not shift as quickly as the product development technology.  In reality, perhaps only 1 in 5 of such policies would sustain to an insured’s average life expectancy based on a planned premium estimated at 12 percent gross return. Why such a big difference?

All universal life policy illustrations are calculated based on current rather than guaranteed assumptions.  Crediting rate assumptions may depend on declared policy credits (traditional UL), market gains and losses in chosen equity and fixed-income sub-accounts (variable UL), or credits determined by outside market indices such as the S&P500 over a defined period of time (such as a “1-year point-to-point”) and limited by guaranteed floors and “capped” gains. 

Expenses are based on current (but not guaranteed) experience and are projected on the basis of a current “scale” for insurance charges and other policy expenses.  But the policy illustration treats both the selected non-guaranteed crediting rate and non-guaranteed expense scale as a constant for purposes of projecting planned premiums and/or policy values for as long as 50 to 70 years in the future. 

It is extremely unlikely that non-guaranteed elements will persist as originally projected over such a long period of time. And this is especially problematic when the objective of the client is to acquire the most amount of protection for the lowest possible cost. 

In the 35 years that universal life policies have been available, cost is typically a consumer’s primary consideration, often driving the cost-conscious client away from the initially high guaranteed premiums of whole life, and toward the appearance of lower cost for UL.  Indications of very poor policy persistency rates beyond 10 years are just one lagging indicator that the paradigm shift is still struggling to gain legitimacy for providing low-cost, lifetime death benefits at and beyond life expectancy.

The illusion within the illustration

The gold standard of care for working with a client is to “place the client’s interest above my own.”  That is, for example, the commitment of the credentialed members of the Society of Financial Service Professionals.  But what does that mean in practical terms when a life insurance professional, or an attorney or accountant on behalf of her client, is reviewing a policy illustration for needed lifetime insurance?  Do we accept a static rather than dynamic approach to illustrated values and strive for the lowest premium to “win the sale.” Or do we work toward the solution that has the greatest chance of success? Consider a new case example in which an estate planning client asks her attorney to review several policy illustrations for $1 million of lifetime protection.  While the attorney concurs that the policy amount is appropriate to the client’s circumstances, they are both concerned about the extreme difference in two proposals.  The first suggests that the premium for coverage is $4,473 and projects that the premium will not only be sufficient but will endow the policy 57 years from now at the client’s 100th birthday.  The second illustration indicates a premium of $8,500—almost twice as much to seemingly accomplish the same result

Based on this information and unable to delve further into possible differences in the way the premium was determined, the decision seems obviously inclined toward the lower cost policy.  But of course it’s much more complicated than that!  It’s not about a better policy, but a better illustration!

In fact, both illustrations reflect the same indexed universal life policy with a 0 percent guaranteed floor and a 12 percent current CAP with 100 percent participation.  The first illustration assumed a constant crediting rate of 7.6 percent, a number that had been “back-tested” by the insurance company as having a good, average result when considering “the markets” between 1980 and 2013.  When the illustration software was used to calculate a planned premium with an assumed 7.6 percent crediting rate, the result was $4,473 for the healthy 43-year old woman seeking $1 million of lifetime protection.

The second illustration was for the identical underlying indexed UL policy.  The difference in calculating a lifetime planned premium was that a three-step process was used to validate or adjust the recommendation as to the likely non-guaranteed premium it would take to support the policy over her lifetime:

Step 1: As in the first instance, calculate the same $4,473 lifetime premium with an assumed crediting rate, such as the back-tested 8 percent. Step 2: Make a supplemental calculation by exposing the proposed $4,473 lifetime premium to 1000 unique randomizations of historic volatility in the S&P500 index, with the limits of 0 percent on the downside and 12 percent on the upside.  The result is that none of the 1000 hypothetical illustrations was able to sustain such a policy to age 100, assuming average industry projected expenses for this type of policy. 

Further statistical information included the fact that the first lapse out of the 1000 hypothetical illustrations occurred at age 79 (average life expectancy is age 90 for a group of healthy 43-year old women) and that more than 90 percent of the 1000 lapses occurred prior to average life expectancy.  (Considering that many healthy 43-year old women expect to live closer to 95 or 100 than their current statistical average life expectancy, the fact that the tested premium has a 0 percent probability of success could be a problem for both client and agent).

Step 3: Calculate a planned premium based on the statistical information that would sustain a policy to age 100 at least 90 percent of the time.  The resulting revised planned premium is $8,500 and could typically be calculated in the insurance company’s illustration software by using a crediting rate assumption of 5.18 percent.  (This client-specific, lower constant crediting rate compensates for volatility in this example).

Once the client’s attorney had this supplemental information, she began to realize that her own paradigm shift in awareness was going to be extremely important in her recommendations and endorsement with clients who seek her advice about life insurance choices.  It wasn’t a matter of which policy was better. They were both the same. 

The difference is in how the planned premium expectation should be determined; and then to encourage her client to reassess every few years the reality of changes in non-guaranteed credits and expenses.  In other words, life insurance more clearly becomes a valuable asset—managed for optimum results over the lifetime of the insured—when we know to compensate for volatility in the crediting rate and possible changes in expense charges by starting out with a reasonable (rather than an unattainable) premium expectation. Conclusion

Solomon Huebner, a professor of finance at the Wharton School of Business, was also the founder of the American College (1927) and the Society of Financial Service Professionals (1928).  His lifetime objective was the professionalization of the sale of life insurance. 

It was his view that the “head of the family” had a moral and financial duty to assure the continuity of the family’s lifestyle in the event of premature death.  Then and now, insurance professionals place their client’s interest above their own in helping insure human life value for families, businesses and charities.  These insurance professionals explore the means to advise clients not only that the illustration does not represent a  reasonable expectation, but pursue the means by which they can give clients realistic premium and value expectations that will be managed throughout the insured’s life. 

An important benefit of membership in the Society of Financial Service Professionals is the Historic Volatility Calculator, the tool that provided all the statistical data in this article, and ultimately made it possible to objectively answer the question: “Why would I pay $8,500 for something that I could otherwise have for $4,473?”