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Data exhaust moves beyond targeted marketing and into financial services decision making November 27, 2010

Posted by jeremyliew in data, financial services, marketing, targeting.
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Fascinating article in the WSJ a couple of weeks ago about how the big insurance companies are testing using data profiles to identify risky clients. Using the data is potentially an alternative to the costly physical exams currently used to underwrite health insurance policies:

 

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…

Making the approach feasible is a trove of new information being assembled by giant data-collection firms. These companies sort details of online and offline purchases to help categorize people as runners or hikers, dieters or couch potatoes.

They scoop up public records such as hunting permits, boat registrations and property transfers. They run surveys designed to coax people to describe their lifestyles and health conditions.

Increasingly, some gather online information, including from social-networking sites. AcxiomCorp., one of the biggest data firms, says it acquires a limited amount of “public” information from social-networking sites, helping “our clients to identify active social-media users, their favorite networks, how socially active they are versus the norm, and on what kind of fan pages they participate.”…

Acxiom says it buys data from online publishers about what kinds of articles a subscriber reads—financial or sports, for example—and can find out if somebody’s a gourmet-food lover from their online purchases. Online marketers often tap data sources like these to target ads at Web users.

Not everyone is comfortable with this approach. Some regulators have raised potential concerns:

“An insurer could contend that a subscription to ‘Hang Gliding Monthly’ is predictive of highly dangerous behavior, but I’m not buying that theory: The consumer may be getting the magazine for the pictures,” says Thomas Considine, New Jersey’s commissioner of banking and insurance.

I think I’d bet against Mr. Considine on this one.

I’m fascinated by the idea of using publicly available data to make better underwriting decisions, whether for insurance or for lending. This isn’t a new idea. Student loans first became a growth industry when someone decided that using a students major (pre-med vs liberal arts) or GPA could help them decide who to lend to and how much. But as the amount of data available has exploded, whether directly reported (e.g. on social networks), inferred from behavior (e.g. web surfing and ecommerce habits) or volunteered as part of an application (e.g. bank account log in info, as supplied to Mint, that can show regularity of income and cash payments), a financial instituions ability to underwrite more individually goes well beyond FICA scores.

I’m interested in any companies looking at doing something like this. Email me.

 

Comments»

1. Sherman Unkefer - February 1, 2011

It is a very different risk to venture if we base our activity on the statistical data collected and not on the actual interviews that we previously had done. But everything has its risk and I think this is a better way since it is supported by fact itself. Great post.
Sherman Unkefer

2. Big Data + Machine Learning = Scared banks. | PandoDaily - February 27, 2012

[…] exists to apply big-data machine-learning to improve underwriting in insurance, as I have noted before. Share this:PrintEmail Jeremy Liew Jeremy Liew is a managing director at Lightspeed Venture […]


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