#199 | John Hancock Accumulation IUL 19 – Part 1
In my Intro to Econometrics class in college, one of our first projects was to gather our own data on a particular topic of interest, plug it into SPSS, match the data with a statistically-significant least squares equation and interpret the results in a paper. I chose house prices in my home city of Charlotte, which seemed like a simple idea that only bared its teeth once I got into the data and realized just how many factors go into the price of a house. I collected sales data over the past 6 months with certain variables about the houses and I started to work that data to death. Add variables, take away variables, define new geographic boundaries, create new variables, you name it. Finally, I had managed to push the statistical significance of the equation up to something that I thought would pass muster in class and, I dare say, I was proud of the blood, sweat and tears I’d poured into that R-squared figure.
Unfortunately, I’d made some tradeoffs that were only obvious to me after the fact and I had time to reflect on what I’d done. My final equation was so complex and convoluted that I could barely explain it or the suite of ratio-driven variables that went into it, which meant that I had very little intuitive feel for how the equation actually worked and the results that came out of it. But even more problematic, the equation was tuned to fit a specific set of data that was built a specific way over a specific time period. The equation might have completely failed if presented with new data. So while I’d created an equation that produced a very nice fit to the particular data I’d collected, my equation was effectively worthless. I couldn’t explain how it worked, had no intuitive feel for the results and didn’t know what it would do when untethered to my limited dataset.
Pricing a life insurance product is like my econometrics project, only vastly larger and more complex. The insurer has to solve for their own complex and multi-layered definitions of profitability, compensation and competitiveness by building an equation made up of traditional policy charges and credits. There is no perfect solution, only tradeoffs. Insurers are constantly pivoting their equations in order to create more desirable tradeoffs at any particular moment based on internal goals and market pressures. But just as in statistical analysis, building a more complex equation can produce more desirable tradeoffs in the model – in other words, creating more complex products can yield better results in competitiveness, risk profile, profitability and compensation simultaneously. What’s the tradeoff? The ability to simply and intuitively explain how the product works, predict what it will do and manage it in the real-world. Put succinctly, making an equation that better fits the data almost always means losing some measure of the ability to understand and explain it.
No company has embraced the idea of building hyper-complex products to form-fit the data more than John Hancock. Back in Guaranteed UL days, John Hancock sported some of the most complex multi-tiered shadow accounts in the industry. Their traditional Universal Life product, Performance UL, gradually began to incorporate a COI-adjustment mechanism based on funding patterns that was unique in the industry. The successor to Performance UL, Protection UL, introduced the Persistency Credit, a virtually inexplicable product mechanism that sent half of the industry in a tailspin over its lack of transparency and complexity and the other half of the industry running to their clients to sell the most “competitive” product available. In fact, if you really look hard, every single John Hancock offering has some unique, non-traditional pricing mechanism embedded in it that makes it perform extremely well in certain situations. Accumulation IUL 2019, the subject of this review, is no different.
I bring all of this up to make a point – I never get more frustrated reviewing any product in the market than a John Hancock product. All John Hancock products follow the same pattern. They are incredibly competitive on the illustration and they have extremely complex internal mechanics. They are form-fitted equations that are seemingly unexplainable and unintuitive, so much so that I’ve witnessed plenty of actuaries struggling to explain the results that come out of the products. If I were to write an article about what the product does, it would be a very simple and straightforward article. Of course it performs well, whatever that means. But that’s not what I do. The Life Product Review has always been dedicated to writing about how products work, not describing the results they show on the illustration. You can see that for yourself. What you can’t see for yourself is how all of the internal mechanics work behind the scenes to make the performance come to life and why it matters for you and your clients. That’s why John Hancock products are so frustrating to me. I know, when I review a JH product, that I’d better block off a full day (in this case, a couple of days) to reverse engineer the product so that I can fully understand how it works, even if I’m not exactly sure what internal datapoints John Hancock was trying to solve. In the end, that doesn’t matter anyway. The product is the product. And it’s my job to tell you how it works.
However, despite the fact that I think understanding how this product works is essential to selling it, AIUL19 does tell a fairly simple story in terms of illustrated performance. If you cut it to the bone, here’s the deal – AIUL19 is one of the most expensive products in the industry in terms of fixed charges, even moreso in a high-funded scenario, which results in some of the worst cash values in the industry in short and medium durations. The only way that AIUL19 digs out of the hole and produces stellar cash value and income projections is by using a complex combination of aggressive caps, charge-funded multipliers and dynamic interest bonuses. You can already see the where the complexity lies. Why, exactly, does AIUL19 become demonstrably more expensive in a high-funded scenario? That’s the subject of the next post, Part 2. And what, exactly, is this complex combination of crediting factors that produces such stellar long-term performance? That’s covered in Part 3.
Before I get there, though, I want to hit the other big update to AIUL19 that is worth mentioning. John Hancock has finally introduced two Base account options that do not have an asset-based charge or a multiplier, which compliments the existing Standard (2% charge) and Enhanced (5% charge) account options. The addition of the Base account sheds a little bit of light on how the account options truly compare to one another. For example, the Base S&P 500 account has a 10.75% cap but the Standard and Enhanced S&P 500 accounts only have a 10% cap, finally revealing that there’s more going to fund that the multipliers in the other accounts than just the asset-based charges. This plays into both sides of the core John Hancock story that its Accumulation IUL products are best-in-class at maximum illustrated rates and hold up well under “lower” illustrated rate scenarios as well. Think about the math – by artificially suppressing the cap, Hancock is essentially shaving off a bit of upside illustrated performance to increase illustrated performance at lower rates, which is a choice they can make because they have a higher option budget than the other insurer in this example. And if this other insurer happens to be, say, Pacific Life limping along with a 9% cap (effective in March), then that puts John Hancock in a very enviable position at the moment in terms of illustrated performance. But if you want a sense of how things may play out in the real world, read this.
Now, on to the fun stuff.