Let’s face it, no one wants to pay any more than they have to for life insurance. So it’s no wonder that when shown a spreadsheet of calculated planned premiums for comparable policies, the customer’s inclination is to select the product with the lowest shown outlay. If it works for choosing flat-screen TVs with similar screen qualities, it should work for life insurance. Right?
What we know from a behavioral standpoint is that price is the logical tie-breaker when not much else is known or can be appreciated about a TV — or a sophisticated life insurance policy intended for lifetime use.
Further exacerbating the problem of “pricing” a modern life insurance policy, indexed policies are perhaps the most complicated form of universal life, incorporating many moving parts and many hidden costs that will vary over time. Planned premiums are almost always based on a calculation assumption of constancy (the illustrated rate chosen by the producer — up to what the carrier allows in the software). Meanwhile, the success or failure of the policy over time depends on the sequence of returns within a “collar” of a guaranteed minimum crediting rate (often 0 percent) and the cap/participation rate.
While we know indexed universal life’s producer-specified illustrated crediting rate assumes a linear constancy, in reality IUL products will be significantly impacted by market volatility and will more likely be an “all or nothing” pattern within each portion of account value. Expressed over long periods of time, the uneven returns — and factoring in the policy’s internal costs, such as mortality and expenses — produce a sequence of returns and charges. This sequence becomes the driving force of the policy toward success or failure to sustain coverage as long as the insured is alive.
After a number of years of testing, we can say with certainty that when the planned premium is based simply on the highest illustrated interest rate a carrier allows, the illustration may win the day – but the client and ultimately the producer will lose. Such an outcome isn’t intentional. As insurance professionals, we haven’t had a better way to assess the difference between policies based on their calculated planned premiums. Until recently!
If we told you the lowest-cost product in a spreadsheet of eight similar top products had a 20 percent probability of sustaining to the age illustrated or — put another way — has an 80 percent chance of lapsing prior to the last illustrated age, would you recommend that product to your client? Of course not.
We can make such a calculation using Monte Carlo assessments — often used by financial planners to determine the likelihood of success for a strategy in which the future outcome can’t be known. Now it’s possible to provide supplemental information about planned premium calculations that opens up the last lid on the black box of IUL.
The key to this new approach of not just calculating a planned premium but also assessing its long-term sustainability is first to explore with the client their risk tolerance for a policy not achieving its lifetime objective. Is it 0 percent? Ten percent? Twenty percent? We find that even normally risk-tolerant investors will rarely accept more than a 20 percent risk that a life insurance policy might not “live” as long as they do; after all, it’s life insurance for the financial security of a family or business.
An IUL planned premium of $8,797 for a healthy 47-year old-male was calculated using 6.48 percent — the maximum rate under AG49 for 2016 with a 0 percent guarantee and an 11 percent current cap. As in Chart 1, the calculated premium “illustrates” carrying the policy to age 120 — yet assesses under a Monte Carlo calculation as having a 50 percent or more chance of not sustaining to age 100. And if we lower the cap assumption to 10 percent, that increases the risk of lapse prior to age 100 to almost 70 percent.
If the client chooses their appropriate tolerance for lapse at 90 percent, the correcting premium is $10,500 for the first several years until there’s more actual experience with account value growth. In addition, the funding level can be managed based on how well the index, expenses and sequence of returns are actually performing.
A New Paradigm
So the most common approach to selecting the best IUL policy — “what’s the best price?” — conveys the most risk! In the new era of Department of Labor requirements for suitable product and funding recommendations, we need to redirect the client’s “price” mentality to a more appropriate conversation. With two simple one-page charts, here’s how that conversation might sound.
Producer: “Using this simple approach to matching your general risk tolerance to a type of policy you’re likely to find fits your money ‘style,’ an indexed universal life policy would appear to meet your profile for lifetime life insurance. So the next issue to address is how to best calculate a planned premium.
“When considering a long life, what’s the balance of ‘low’ premium and the likelihood the policy will last as long as you do? Ten percent? OK — based on your requirement — we should start with $10,000 a year for at least the first two years, and then re-evaluate to see if we’re on target. Does that make sense?”
How many policies should be explored so the client doesn’t get concerned we have biases for one particular carrier? There are differences between IUL policies — but exactly how those differences will manifest on behalf of the policy owner won’t likely be known for a number of years.
Carrier financial strength is important, as is experience with policy service and customer focus. We don’t know which policy among a field of contenders ultimately will be best. That’s a function of too many variables, more than we can predict today — including the degree to which the client follows the payment of the recommended planned premium!
If a lifetime commitment seems like too much commitment, we will occasionally provide two payment scenarios: 15 years and life. Not only might this better match the client’s sensitivity to paying “forever,” but in some policies we may discover how different payment schedules affect the outcome. For some, there is a clear indication that the contract works better paying more for fewer years, where for some there is no discernible difference. This is an indicator of how the contract was designed. Clients can choose the product that works best for the payment scenario they desire.
“But if I’m going to be ‘quoting’ a higher premium than the competition, I’m going to lose the sale!”
With policy information presented in this manner, you have no competition. This sale is no longer about the lowest price an agent can illustrate. Any competitor would necessarily have to answer the same questions about probability of success. And any illustration can be run through the same system for comparison. The sale then becomes more about success than price.
Given the choice, what client would choose a product with a 70 percent chance of lapsing before life expectancy? This process focuses the client on the right metrics and challenges them to consider the consequences of underfunding.
What Are the Results? Three Very Important Things:
• First, your clients will have been given enough data to make a decision that will not come back to haunt you later. In the language of fiduciary, we help clients determine what’s in their best interests.
• Second, clients will agree to pay more in order to increase their chances of success, thus increasing commissionable premiums.
• Last, but not least, you will have no competition because simply showing a lower-cost illustration will not be enough for clients once they are exposed to probability testing. This is a huge win/win scenario!
This idea works best for clients who need a death benefit at an affordable price. Overfunded IUL used for retirement income purposes is a different animal, but one with similar issues. Obviously since those policies are already overfunded, the issue becomes the probability of receiving the anticipated income. Here you may face the opposite side of the risk equation: Will the sequence of returns have a negative impact on the anticipated income?
No matter what the situation, life insurance policies with variables that have a material impact on the ultimate success or failure of the contract are being sold routinely to clients who have no basis for understanding the implications of options pricing, market volatility, non-guaranteed cost of insurance rates and premium payment management.
Using a Monte Carlo simulator to assist in the evaluation of product suitability ensures that you will sell more IUL, at higher premium levels, with a higher degree of customer satisfaction.
Ron Sussman is founder and chief executive officer of PolicyAudits.com and CPI Companies. He counsels high-net-worth individuals through risk management analysis and life insurance planning strategies. Ron may be contacted at [email protected]
Richard M. Weber, CLU, MBA, AEP (Distinguished), is past president of the Society of Financial Professionals. A 45-year veteran of the life insurance industry, he is a consultant to insurers and their agents on the topic of effective and ethical selling. Contact him at [email protected]