Insurance Companies Using Electronic Data About Lifestyle and Purchasing Habits to Generate Risk Profiles
November 26th, 2010Via: Wall Street Journal:
Life insurers are testing an intensely personal new use for the vast dossiers of data being amassed about Americans: predicting people’s longevity.
Insurers have long used blood and urine tests to assess people’s health—a costly process. Today, however, data-gathering companies have such extensive files on most U.S. consumers—online shopping details, catalog purchases, magazine subscriptions, leisure activities and information from social-networking sites—that some insurers are exploring whether data can reveal nearly as much about a person as a lab analysis of their bodily fluids.
Life insurers are testing new ways to predict life expectancy and they’re mining personal information online and offline to do it. WSJ’s Kelsey Hubbard talks to reporter Leslie Scism about the brave new world of online actuarial research.
In one of the biggest tests, the U.S. arm of British insurer Aviva PLC looked at 60,000 recent insurance applicants. It found that a new, “predictive modeling” system, based partly on consumer-marketing data, was “persuasive” in its ability to mimic traditional techniques.
The research heralds a remarkable expansion of the use of consumer-marketing data, which is traditionally used for advertising purposes.
This data increasingly is gathered online, often with consumers only vaguely aware that separate bits of information about them are being collected and collated in ways that can be surprisingly revealing. The growing trade in personal information is the subject of a Wall Street Journal investigation into online privacy.
I recently came across an article at Lifehacker (http://lifehacker.com/5697167/if-youre-not-paying-for-it-youre-the-product) that touches on this topic. The article itself says very little but it’s the comment by a user named “when” that really raised my eyebrows. I’m going to post the comment in full as it’s already getting harder to find and may be “down-voted” into digital oblivion . . . I hope that’s okay!
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“I’ve posted this before so I might as well do so again:
As a consultant, I wrote a whitepaper for $INSURANCE_COMPANY that discussed using social network data to refine their premium calculation metrics. The essence of it was that people by themselves do not give an adequate portayal of the risk they represent, as the high-payout-low-occurrence events simply do not occur all that often in a person’s life so they are hard to accurately insure against, but knowing who their friends are helps immensely.
Within that person’s *group of friends*, an order of magnitude more accurate assessment of their risk can be made. If your friend gets a DUI, your apparent health and auto insurance risk goes up because you’re more likely to drink and drive yourself even if you haven’t been caught yet. If your friend gets a drug arrest, your homeowner’s insurance risk goes up because you’re more likely to be the victim of a breakin or assault.
It goes beyond that too – searching for drug use keywords (“drunk” or “hungover” or “420” or “bong”) within someone’s Facebook update stream gives you a much more accurate portrayal of their drug use than a questionnaire, and you can accurately weight the impact of hazardous behavior keywords on friends’ insurance premiums based on their level of interaction. Other keywords, like liking certain musical acts could be similarly useful. (Phish versus the New York Philharmonic) Knowing someone’s employment status often is valuable in assessing credit risk, so keying on “fired”, “laid off”, etc. might be useful.
The same report would also be useful to credit card ratings agencies – if your friend has bad credit, then that should probably have a negative effect on your credit score.
They payoffs are potentially *huge* for insurance companies and banks for whom assessment of risk is their entire business, so much so that they would be stupid not to propose data-sharing arrangements with social networking sites.
I do not know what they did with the report, but it was well received and I got paid. I specifically said that I would not accept any follow-up work along what the paper proposed. 🙂
The report was written before the 2010 July 100M Facebook account leak, and I would imagine that if anyone at all read that report and considered it useful, they hopped
on that torrent post haste to run some quick analytics and backtesting.”
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Wow . . . just wow! If I were to foolishly venture onto facebook right now I would most likely find a large number of my ‘friends’ bitching about being hungover, broke and in debt in excruciating detail.