Spend anytime looking around the internet for reasons not to buy whole life insurance and you’ll inevitably land on pages claiming that 80% or more of whole life purchasers cancel their contracts.
The inference here is that with such a high rate of cancellation those who bought before you learned something you’ve yet to uncover, so save yourself the time, money, and heartache and just skip on down to the next idea.
Sounds reasonable…if only it were true.
It seems as though the internet has allowed us all to become an expert in any field we’d like while bypassing the costly and time consuming process of actually gaining experience in a field.
I can’t say precisely where this 80% cancellation statistic comes from, but I have a sneaking suspicion that, for many, it comes from this post we wrote a couple years ago.
To my knowledge, we are the only source that has ever placed a specific number on claims rates for whole life insurance. Believe me, we've made several attempts to gain additional information on this subject with no result. Additionally, the statistic we quoted is certainly similar to the so-called cancellation rate many are quoting.
I’m fine with anyone choosing to cite my information, and I’m also fine with anyone taking my data and making additional inferences from it. I make tons of inferences based on data I collect from various sources.
But there are several problems with this particular inference.
If you go back to that post you’ll notice that I mentioned I got the number from an actuary speaking specifically from his experience. While we can infer that his employer is likely similar to other insurers in the same marketplace, we cannot know this with certainty.
Further, as I originally noted there could be a multitude of reasons why an insurer might covet or work intentionally to create a scenario where whole life policies end without paying a claim and this can be mutually beneficial for the insurer and policyholder.
This is a tad nuanced (I suppose) but the insurance industry (specifically the life insurance industry) counts termination of a policy regardless of what one does immediately after cancelation.
This is important because “canceling” an insurance contract could just be a necessary step in buying a new one. An example will help clarify this point.
Let’s say the universe only consisted of 10 people.
All 10 of these people bought whole life policies. Then all 10 bought new whole life policies by transferring the cash accumulated in their old policies to their new policies via the 1035 exchange option.
Then five of these people decided to transfer the cash in their policies to annuities. Then two of the policy holders decided to transfer their cash again into single premium life insurance policies to guarantee a death benefit but not pay any additional premiums. One of the remaining three decides to cancel the policy for cash.
Assuming no further changes are made and they all die:
What’s the payout rate given all policies issued? Roughly 14%.
How many policyholders received a benefit in cash of some degree from having had a policy? 100%.
We don’t ultimately know what percentage of whole life policies are actually cancelled. We have a small sample size data point, but I cautioned way back when I first discussed this subject its limited usefulness.
Further, and way more importantly, I don’t know why it matters.
The 1% term life insurance payout hasn’t stopped—nor should it stop—many people from buying term life insurance to protect their families and other financial interests.
Using this statistic to steer people away from whole life insurance (or any form of permanent life insurance) is grossly misleading and extremely irresponsible. It further underlines a serious lack of understanding the life insurance industry and how this data is collected/reported.
Brandon launched the Insurance Pro Blog in July of 2011 as a project to de-mystify the life insurance industry. Brandon was born in Northern New England, and he currently calls VT home. He attended Syracuse University and graduated with a triple major in Economics, Public Administration, and Political Science.