Regulation? WHAT regulation?!
NAIC’s AG49 went into effect Sept. 1, 2015 for all Index UL policies sold on or after that date. It’s a “guideline” – which means became effective without any further discussion once the NAIC’s Plenary voted for it. For brokers, the impact is that policy illustrations may not be calculated with crediting rates exceeding a range between 6.03% and 7.63% (for 2016), depending on a policy’s current “Cap” rate – ranging from 10% to 14%. As of March 1, 2016, the final part of AG49 regulations went into effect regarding the assumed maximum “spread” that may be illustrated for variable loan rates.
How does AG49 affect the typical broker?
It really doesn’t affect brokers unless they’re running illustrations at unreasonable projected assumed rates of return!
Well – how would you define unreasonable?!
Virtually all IUL policies primarily or exclusively use the S&P500 as the referenced index for determining the point-to-point year’s crediting rate – and it’s the most popular index selected by policy owners. That index has averaged 9.6% in the last 25 years, so most producers didn’t seem concerned about illustrations allowing crediting rates in the 7.0 – 8.5% range. But even 7.0% is unreasonable!
OK – why is 7.0% UNreasonable?
First of all, New Years Day when you hear on the news that the S&P500 was “up” 13.68% for 2014 – that’s strictly a calendar year quote. It could be quite different if you were measuring back from some other 365-day perspective. For example, we isolated 4 different one-year point-to-point look-back dates measuring from 2011 to 2010 in the month of August – and found that with a 0% guarantee and 12% current cap, the resulting crediting rate would have been 0% or 3.79% or 8.79% or 12% … all within a 10-day window of time! That’s an extreme but real example of how point-to-point returns can and will vary from what we typically hear as the annually reported calendar year return.
Secondly, 2014’s calendar year S&P500 “return” of 13.68% included a calculation of reinvested dividends for those 500 large-cap companies measured by the S&P500. The rate WITHOUT dividends at the end of 2014 was 12.39% – a difference of 130 basis points. And that’s important because the Index used by the insurance companies in support of Indexed crediting rates is the one without dividends included.
So why is 7.0% unreasonable? Because the S&P500 without dividends is barely over that number – and that doesn’t account for the years the Index will be negative – for which the crediting rate will (typically) be 0% depending on the policy’s underlying guarantee.
OK – so what’s a REASONABLE crediting rate assumption for an IUL illustration?
My generic short answer is 5%. And of course that’s not a number that most brokers want to hear.
How did you get to 5%? Most companies have “rate translators” and they seem to imply much higher historical rates!
What these calculators don’t take into account is sequence of returns and the fact that there is still volatility between the guaranteed rate and the current Cap. And unlike a straight investment accumulating value over time, volatility in an insurance illustration can force more net amount at risk into the policy calculation in order to maintain a constant death benefit. As the insured gets older, the charges for the extra, unplanned net amount at risk start to add up and can make the difference between success and failure of a long-term strategy to maintain life insurance for a lifetime.
So I guess my question should be: what’s the RIGHT crediting rate for any particular client – and how do you come up with it?
My actuary partner Chris Hause and I developed proprietary software almost 20 years ago – first to deal with the effect of declining UL interest rates – then to address volatile VUL crediting rates – and about 3 years ago we applied our process to IUL.
For data input we start with a VUL or IUL sales illustration and its solved-for planned premium and/or future cash flow withdrawals. But rather than “accept” the 7.0% crediting rate so often defaulted in IUL illustrations, we independently – and quickly – calculate 1000 hypothetical variations – all with the previously illustrated planned premium and other illustrated outcomes – to see what happens when we randomize the returns rather than just assume a constant rate. Eight seconds after pushing the random button, we see how many of the 1000 “illustrations” made it to age 100; the age the first lapse occurred; the average index return calculated within the randomized illustrations; and the distribution curve of the hypothetical lapses before age 100. A lot of data – but the bottom line is that it tells us the probability of success of any given illustrated proposal.
What’s an example of your process?
PacLife is a major manufacturer of IUL. And their IUL policy illustrations typically default to rates most brokers believe reasonable. So I requested their “Pacific Indexed Accumulator 5” on behalf of a healthy 47-year old male wanting $1 million of protection – calculated for a level annual premium for the rest of his life – pure vanilla protection. The planned premium was $8,797 based on the maximum “AG49” illustration rate of 6.48% for an 11% current Cap. The age 100 account value with this premium, projected current expenses, and a constantly assumed 6.48% crediting rate is $1,000,000, and with no further premiums required past age 100, the policy is projects to remain in force to age 120. If death didn’t occur until age 120, the death benefit projects to $3.6 million.
We took the policy illustration assumptions (to age 100) and entered them in our Historic Volatility Calculator for IUL and gained some additional insight. We don’t challenge PacLife’s numbers – they’re not doing anything wrong – we just add some information you wouldn’t have otherwise known from any carrier about the planned premium calculation.
What was that additional information?
When we ran 1000 hypothetical illustrations based on the illustrated numbers and randomly selecting from a database of almost 16,000 historical S&P500 1-year point-to-point returns since 1951 – we found that approximately 570 of those illustrations sustained to age 100 – but 430 did not.
What does that imply when you say 570 hypothetical illustrations didn’t sustain to age 100?
In that quick calculation of 1000 hypothetical illustrations, 430 lapsed before age 100. The first lapse occurred at age 88 – which while the average life expectancy of a healthy 47-year old, many people expect to live longer than their peer’s average. This difference between our calculations and that of the carrier’s illustration is the difference between an assumed constant rate of return – and our methodology of randomizing historic returns and then “collaring” the results between the current Cap and the guaranteed floor.
So are you saying in that example that “43% of the time” – the policy won’t reach age 100 even though the illustration seems to suggest that it will sustain to age 120?!
Yes – and we’re not blaming the carrier – we’re highlighting why policy illustration regulations – last considered 20 years ago – can’t tell the whole story without supplemental information in the hands of the producer for current assumption policies. In turn, without more relevant information than illustrations themselves allow, the producer can’t meaningfully help the client make a planned premium choice that makes sense for her.
I have to assume a client who comes to understand that the recommended planned premium has only a 57% chance of carrying the policy to age 100 – an age that a healthy 47-year old might think possible – that’s going to be unacceptable!
Right! But let’s turn this to our advantage. Let’s acknowledge 57% is not acceptable – and ask him what “success probability” would be acceptable? Since the 2008 economic debacle, the answer is often at least 90% – and half the time we hear the client require 100%. With that information – I can make a calculation of a planned premium that is not the lowest premium for a given amount of coverage – but rather the planned premium that will meet the client’s long-term objectives of a sufficient policy that will last at least 45 days longer than he will – no matter how long he lives!
Can you give us an idea of how you specifically explain that to someone who’s only consideration about life insurance pricing is that no matter how cheap we try to make it – that’s too much?!
I can go through a simple explanation of why lifetime needs can be fulfilled only by policies that are actuarially designed to be affordable for that lifetime – as opposed to term for which the design is to make it UNaffordable at just the point when most people will die. Beyond that, I tend to run a few additional custom calculations with our software that associates 80% – and 90% – and 99% “success probabilities” with the applicable planned premium that statistically produces that probability.
So for the 47-year old and the $8,797 planned premium – with only a 57% probability of success – I demonstrate the higher premiums with the higher probabilities – and also show the “earliest lapse” – and their life expectancy “peer group’s” average LE – and let the client make their own choice!
For example, if you didn’t like the success “odds” of 57% on the $8,797 premium – if you’re comfortable with 80% – that takes roughly $1,000 more taking it up to $9,500. 90% – takes only another $400 to $9,900. But it will take $11,500 to bring the “odds” up to 99.9%. “What’s your comfort level?”
You’ve described your process as statistically valid – so are those numbers “hard” numbers & guaranteed to produce the results you show?
Absolutely not. They’re statistical estimates with very little prediction power. But that’s the very reason stochastic analysis was developed – more commonly called Monte Carlo – because we don’t yet have enough data. So I need to clarify several things about the analytical process I’ve been describing.
- It’s not But “our” calculated planned premium for a given probability of success will create a more realistic expectation than the constant return assumption of the illustration if the client is looking for “best price,” and
- Our technology is most useful when it’s reapplied every few years – as actual account value is accumulated and a revised in-force illustration can be evaluated by the technology. If the stock market “gods” have been smiling on you, maybe you can pull back on your planned premium a little bit or take a premium holiday once in a while. On the other hand, maybe the markets have turned sour at the wrong time – in other words the sequence of returns is working against you, especially if the design includes cash flow withdrawals and loans in the future. The higher probability-driven planned premium may have to be increased, but likely only somewhat more – as opposed to disastrously more the way I have seen so many policies turn out.
Our bottom line conclusion has always been that modern life insurance is just like any other sophisticated financial product – it has to be appropriate for the client’s needs and it needs to be managed over the insured’s lifetime. If you don’t do that – the policy has an extremely low probability of paying a death claim at average life expectancy.
And by the way, I call myself the “pay me now or pay me later” consultant – because I also do a lot of litigation support on behalf of policy owners who are not amused with the 60-day letters they get demanding substantially more money to keep the policy in force even for just another year – after dutifully paying the original planned premium that was unfortunately based on an unrealistic initial crediting rate – which the agent doesn’t go back to provide correcting advice!
Well, this is great technical information, but perhaps a bit much to take in for many clients. Following your explanation of the problem with volatility even in IUL – why not just use a really low crediting rate assumption that you know will almost always work?
You absolutely could do that – and that gets back to my brief answer a while ago when you asked me for a “reasonable” crediting rate – remember my answer?! 5%. And we find that’s typically the crediting rate if you solved for a planned premium in the illustration software – that premium solved at 5% would test out in our calculations as having a 90% or better probability of success.
From a practical standpoint – how does the producer defend his sophisticated approach to recommending a successful but HIGHER planned premium compared to “the competition” – who just preys on the typical consumer’s desire for best price?!
That’s a practical and important question. Clients will nod their heads and understand why it’s in their best interest to pay $9,900 rather than $8,797 – until some other agent comes along and tells him he can “get it for you cheaper.” And they’ve forgotten WHY they bought the policy OUR way 5 days after he paid the first premium.
So I suggest to the producers with whom we consult that you should leave behind a “Buyer’s Remorse” kit! The kit consists of the original illustration suggesting $8,797 and a handwritten note on top that says “I would have preferred to pay this – but I understand it has an unacceptable likelihood it will keep the policy in force as long as I live.” On the second illustration – with the planned premium that is consistent with his “success probability” – the note on top says “I’ve chosen to pay $9,900 a year at least until we can re-assess how well we’re doing – because that’s the smart way to manage the policy.” Hopefully he’ll look at the kit when that slick agent tries to twist the policy away – and hopefully he’ll call our producer before making a big mistake!
So – you’ve got software! How would a producer gain access to it?
Insurance actuaries use the underlying statistical process all the time for new product pricing and modeling – but companies have been very resistant to adopt it within policy illustrations without regulatory authority. So Chris and I decided to put the technology in the hands of the producer as a supplemental tool to help the producer find the answer to the question: “what crediting rate should I use” to model a possible policy outcome. Several years ago we donated the Historic Volatility Calculator to the Society of Financial Service Professionals – and anyone who is a member in good standing has free access and use of the calculator. While FSP membership eligibility was once restricted to those with a CLU or ChFC designation, the Society now ALSO embraces professionals as diverse as attorneys, CPAs, CFPs and 15 other designations and degrees – and only requires matriculation in any of those designations to qualify for membership. E-mail me at << Dick@InsuranceFiduciary.com >> and I’ll send you a membership application!
Any final thoughts about AG49 and how we’ll deal with it?
- Use a reasonable crediting rate for calculating planned premiums – either with the Historic Volatility Calculator or just a low initial rate such as 5% – and there’s nothing to worry about.
- But with all our sophisticated calculators, I have a concern that the reduction of illustratable rates will encourage producers who are not listening today to think that because the NAIC has “dealt” with egregious crediting rates – the new guidelines are “OK” and appropriate for new sales illustrations – and I think I’ve explained how that just isn’t the case!
- Further, the illustration rates the NAIC was concerned about rarely touched a carrier’s current Cap – so Caps weren’t the issue – just the much lower average crediting rates! BTW: caps are coming down! All things being equal – and they rarely are – it’s likely that a return to so-called normal interest rate levels over the next few years will increase the option costs of insurers offering these policies – which I personally predict will cause Caps to come down to 10% or less by the end of the decade. That’s not a bad thing – carriers have to protect their financial strength – but there has been an evolution to the various types of universal life over the last 35 years, and the “next best thing” in life insurance is waiting to respond to whatever the next turn in the economy might bring us!