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Data mining being used to set insurance rates, critics cry foul

call foul basketball

Insurance is a different kind of product. You need it to get a mortgage and you're supposed to have it to drive a car. That's why the industry is highly regulated.

You might not like the price you pay for your home or auto insurance policy, but by law those premiums must be based on actuarial risk—the expected cost to provide you that coverage.

Some insurance companies now use sophisticated software to help them set their rates. The industry says the process, called "price optimization," is simply a way to be more efficient. Consumer advocates believe it's being used to get around risk-based pricing.

The Consumer Federation of America (CFA) and the Center for Economic Justice call price optimization an "unfair and discriminatory" way to overcharge policyholders and they want the process banned.

Robert Hunter, CFA's director of insurance, says price optimization is a data mining tool that lets insurance companies figure out which groups of customers are more likely to accept a price increase and which are more likely to shop around for a new policy.   

Hunter, a former Texas insurance commissioner, claims this allows insurers to predict if they could get away with higher rates on low-income customers who have fewer market choices because of factors such as where they live, their socioeconomic status or their financial literacy.

"What we're seeing here is a way to take advantage of the fact that some people don't shop for insurance and that's wrong." Hunter said. "It produces unfairly discriminatory rates which are illegal."

If you have two people with the exact same risk factors, Hunter explained, they should be charged the same rate. But using price optimization, the customer who is tagged as the one least likely to switch carriers in the face of a rate hike would be charged more.


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.

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It took a little while for the insidiousness of this to sink in:

If you have two people with the exact same risk factors, Hunter explained, they should be charged the same rate. But using price optimization, the customer who is tagged as the one least likely to switch carriers in the face of a rate hike would be charged more.

Because of the compulsory nature of insurance in many cases, insurance companies should only be able to vary rates based on generally accepted actuarials plus minor things like how often you are billed.  This is totally abusing their position.

They are abusing their pseudomonopoly position when they see they can get away with it.  They should be confined to varying pricing based on actuarial classes. The too-much-information problem seems more about revealing which actuarial class you are actually in.  Smoking vs. non-smoking seems valid.  Other distinctions might not be or might be counterproductive or otherwise unfair.  For instance, if something is worse only a percentage of the time, it may be questionable to use it as an excuse to charge the other fraction higher rates.  And perhaps at some point, it is simply unfair to not spread risk.  Having certain genes for instance.  We do distinguish between male and female already; it's not clear that should be extended to random genes and what we currently think that means.

Agreed that this is abusing their position and yet they're not being policed.

From  a thread in LinkedIN:

David Webb

This has always been a risk but in UK there is a strong argument that market competition, ease of switching and high customer acquisition costs mean such a strategy is short term. There is a question about what is the actual risk cost! If an insurer invested in better understanding of customer risk than its competitors what rate should it charge? In a competitive market what should it benefit from its capability? Also lots of questions about what is insurance: is it a social good - risk being pooled and the administration outsourced to insurers competing on efficiency; or a competiive market where insurers compete across price, brand, convenience, service and product -as any other market?

Pierre-Emmanuel Lefebvre

Nice summary of the questions raised Dave. Same here in France plus an additional one for mutual insurers on how they will embrace this new era of data mining (beyond their guiding principles of mutual companies, they are in theory banned from doing such price differentiation).

Gam Dias

Whether in retail, or insurance differential pricing is always going to upset a proportion of the people as soon as they find out they have paid more than someone else. In a mandated 'service' like insurance, there's argument that its compulsory nature should not be exploited. Hence Dave's good question - 'what is insurance?' How I would see this: When an insurer calculates risk, it's of the insured making a valid claim and that calculation is used to determine a portion of the premium. However, in the premium there is also an administrative cost element. Differential pricing is based on risk of renewal - so varying the portion of administrative cost may be okay.

Interesting comments. It does speak to the very question of what insurance is.

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