In the not-too-distant future, John Q. Public might want to rethink swiping his credit card four times a week at McDonald’s or renewing that subscription to Skydiving Magazine if he wants a shot at a decent rate for a new life insurance policy.
Big Brother is creeping further into the life insurance business.
A recent article
in The Wall Street Journal revealed some U.S. life insurers are exploring whether collecting and analyzing consumer marketing data can be used to accurately predict people’s longevity. Could this be a first step toward a gradual phasing out of the costly traditional process of using blood and urine tests to assess people’s health?
It is no secret data-gathering companies are continually amassing extensive dossiers on most U.S. consumers by monitoring online shopping habits and information easily available on social-networking sites. Encouraged by companies such as Deloitte Consulting LLP, a number of U.S. insurers are seriously investigating whether this data can reveal as much about a person as a lab analysis of their bodily fluids.
The WSJ article reports that Deloitte conducted one of the biggest tests for Aviva, looking at 60,000 recent insurance applicants. The article said the test found “that a new, ‘predictive modeling’ system based partly on consumer marketing data was ‘persuasive’ in its ability to mimic traditional techniques.”
The article continues to say “a key part of the Aviva test was estimating a person's risk for illnesses such as high blood pressure and depression. Deloitte's models assume that many diseases relate to lifestyle factors such as exercise habits and fast-food diets.”
We know the P and C market analyzes peoples’ credit reports to help price home and auto policies, so it’s not too far out to think the extensive amount of consumer data available today could be helpful in understanding a person’s true lifestyle.
AIG and Prudential are also identified in the article as other insurers known to be exploring the technology, which Deloitte is aggressively pitching to the market.
Advocates say this kind of analysis could lower insurance costs and potentially eliminate an off-putting aspect of the insurance sale. In the article, Deloitte says insurers could save $125 per applicant by eliminating many conventional medical requirements. Under Deloitte's predictive model, the cost to achieve similar results would be $5.
The data apparently wouldn’t be used to make final decisions about applicants, but say the process would speed up applications from the people who look like good risks while other people would go through the traditional assessment process. This part didn’t make much sense to me. Are they still going to require blood and urine samples before actually issuing a policy? Would insurers really ever solely rely on this type of information to issue a policy – with no medical exam?
There are also regulatory hurdles, as in if this type of analysis be allowable by insurance regulators, and whether it would be subject to the federal Fair Credit Reporting Act. If “adverse action” is taken against a person – such as the decision to deny insurance or increase rates – the law’s provisions would kick in and the person involved would need to be notified.
It would be naïve to think the obstacles can’t be overcome, especially if there is real money to be saved. I for one will keep an eye on these efforts, and be curious to see if or how they can make this work for themselves and consumers alike. I’d love to hear your thoughts via the comment box below.
I also encourage you to read the entire WSJ article for more detailed information about this topic.