Life insurers are testing new ways to predict life expectancy and are mining personal data online and offline to analyze the health and medical conditions of consumers. According to an investigation by the Wall Street Journal, executives at American International Group Inc., Aviva USA, and Prudential Financial Inc., confirm they are exploring the use of consumer-marketing data to create a “predictive modeling” system for insurance applicants.

Making the approach feasible is a trove of new information held by giant U.S. data-collection firms, such as Acxiom, Alliance Data Systems Corp., Experian PLC, and Infogroup, who each have detailed information on more than 100 million American households. Deloitte Consulting LLP, a major backer of the concept, has pitched it in recent months to numerous insurers.

These 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.

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. In this Wall Street Journal NewsHub video interview, reporter Leslie Scism explains how “Insurers Test Data Profiles to Identify Risk Clients”.

Although the personal information sold by marketing-database firms is lightly regulated, utilizing it in the life insurance application process would “raise questions” about whether the data would be subject to the federal Fair Credit Reporting Act, says Rebecca Kuehn of the Federal Trade Commission’s division of privacy and identity protection.

The FCRA’s provisions kick in when “adverse action” is taken against a person, such as a decision to deny insurance or increase rates. The law requires that people be notified of any adverse action and be allowed to dispute the accuracy or completeness of data, according to the FTC. The use of the data also may require passing muster with insurance regulators. Regulators in Connecticut, New Jersey and New York, all home to major U.S. life insurers, say they haven’t been briefed.

More on the Wall Street Journal investigation can be found at, “Insurers Test Data Profiles to Identify Risky Clients“.  Increasingly, some companies gather online information, including from social-networking sites. Acxiom Corp., 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.”

For insurers and data-sellers alike, the new techniques could open up a regulatory can of worms. The information sold by marketing-database firms is lightly regulated. The law requires that people be notified of any adverse action and be allowed to dispute the accuracy or completeness of data, according to the FTC. This type of predictive analysis heralds a remarkable expansion of the use of consumer-marketing data, which is traditionally used for advertising purposes.

“Personally identifiable data from the online world is merged with personally identifiable information from the offline world, every day,” says Jennifer Barrett, Acxiom’s head of global privacy and public policy. She also says that, while Acxiom does store personally identifiable information, it doesn’t store or merge anonymous online-tracking data, such as Web-browsing records. Acxiom says it wouldn’t let insurers use its data to help assess applicants, for fear of triggering the stiffer federal credit-reporting regulations. Infogroup says it isn’t supplying information to insurers for this use.

Units of News Corp., including The Wall Street Journal, supply information to marketing-database firms and buy information from them. “We have strict precautions around confidentiality,” a spokeswoman said.

This isn’t the first use of database mining in insurance. About 20 years ago, data pros found that some factors in people’s credit histories have a strong correlation to claims on car and home-insurance policies. In other words: The better your credit, the less likely you’ll file a claim. Today, most car and home insurers use this phenomenon to price their policies. For this purpose, property-casualty insurers look at people’s credit reports, as opposed to the consumer-marketing databases. Life insurers haven’t changed their general underwriting approach for decades, relying heavily on medical screening.

At a conference seminar, an insurance consultant helped explain Deloitte’s concept by discussing imaginary 40-year-old insurance buyers, “Beth” and “Sarah.” Using readily available data, the consultant said, an insurer could learn that Beth commutes some 45 miles to work, frequently buys fast food, walks for exercise, watches a lot of television, buys weight-loss equipment and has “foreclosure/bankruptcy indicators,” according to slides used in the presentation.

“Sarah,” on the other hand, commutes just a mile to work, runs, bikes, plays tennis and does aerobics. She eats healthy food, watches little TV and travels abroad. She is an “urban single” with a premium bank card and “good financial indicators.”

Can Marketing Data Predict Behavior Insurance

Deloitte’s approach, the consultant said, indicates Sarah appears to fall into a healthier risk category. Beth seems to be a candidate for a group with worse-than-average predicted mortality. The top five reasons: “Long commute. Poor financial indicators. Purchases tied to obesity indicators. Lack of exercise. High television consumption indicators.”

The consumer-marketing data for the test came from Equifax Inc.’s marketing-services unit, since bought by Alliance Data Systems. An Alliance spokeswoman says the company was unaware of the insurance-related test, which was done before it bought the former Equifax subsidiary. Alliance “does not provide its marketing data for such purposes,” she says.

Such predictive models wouldn’t necessarily look for indicators of all diseases, such as AIDS, because the insurer would likely learn about some conditions from the answers on an application. Rather, insurers say a model would tend to look for potential risks such as, for instance, diabetes (from, say, a poor diet). The insurer found that the model consistently yielded results that “closely aligned with those of purely traditional underwriting decisions.”

Deloitte acknowledges the potentially controversial nature of its work. “No matter what their predictive powers may be, any variable that is deemed to create a legal or public-relations risk, or is counter to the company’s ‘values,’ should be excluded from the model,” its consultants wrote in an April paper.

Deloitte isn’t the only firm pushing data-mining for insurers. Celent, an insurance consulting arm of Marsh & McLennan Cos., recently published a study suggesting insurers could use social-networking data to help price policies and aid in fraud detection. A life insurer might want to scrutinize an applicant who reports no family history of cancer, but indicates online an affinity with a cancer-research group, says Mike Fitzgerald, a Celent senior analyst.

“Whether people actually realize it or not, they are significantly increasing their personal transparency,” he says. “It’s all public, and it’s electronically mineable.”

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