Monday, February 13, 2017

Big Data: The End of Insurance as Shared Risk

The roots of the insurance industry go back for several hundred years.  The basis of the business was that while the fate of individuals was not knowable, the probability of death, fire, severe accidents and so on could be ascertained from historical data and used to charge a premium that would cover the expenses from compensating the unlucky and still make a profit.  This was a means by which a large number of people would provide funds to support the few unfortunate ones who would be in need of compensation.  Cathy O’Neil devotes a chapter in her excellent book, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, to some of the changes taking place in the insurance industry.  The era of big data, where a myriad of organizations are busy collecting data about us and putting it to uses we have no control over, has tempted insurers to try to predict not group probabilities, but probabilities for each of us as individuals.  Insurance is transitioning from a shared risk model to an individual risk model.

“With ever more information available—including data from our genomes, the patterns of our sleep, exercise, and diet, and the proficiency of our driving— insurers will increasingly calculate risk for the individual and free themselves from the generalities of the larger pool.”

At first glance this might seem to be a healthy trend.  Shouldn’t the most at risk pay the most while the least at risk are allowed to save money?  O’Neil warns us that insurers change their methods when they figure out a way to make more money.  In other words, the cost of providing insurance to society as a whole will go up, and those targeted as the probably misfortunate will, in fact, suffer misfortune as their insurance premiums skyrocket.  They will not so much be protected by insurance as punished by it. 

O’Neil also warns us to be careful about buying into big data schemes because they are often terribly wrong in their assessments.  A poorly constructed algorithm can become a “weapon of math destruction” if it becomes widely applied.  One still cannot predict an individual’s fate, but it can sort people into ever finer bins of lookalikes.  Your fate is assumed to be the probable fate of your particular group—a sorting process that you have no control over.  Your automobile insurance, for example, could double for reasons that have nothing to do with your driving habits or driving record.

“The move toward the individual, as we’ll see, is embryonic.  But already insurers are using data to divide us into smaller tribes, to offer us different products and services at varying prices.  Some might call this customized service.  The trouble is, it’s not individual.  The models place us into groups we cannot see, whose behavior appears to resemble ours.  Regardless of the quality of the analysis, its opacity can lead to gouging.”

O’Neil tells us of a nationwide study performed by Consumer Reports in 2015 analyzing pricing practices in the automobile insurance industry.  What they discovered was startling—to say the least.

“They analyzed more than two billion price quotes from all the major insurers for hypothetical customers from every one of the 33,419 zip codes in the country.  What they found was wildly unfair and rooted….in credit scores.”

Credit scores?  Consumer Reports provides this information in a subsection of the main report: The Secret Score behind Your Rates.

“Your score is used to measure your creditworthiness—the likelihood that you’ll pay back a loan or credit-card debt. But you might not know that car insurers are also rifling through your credit files to do something completely different: to predict the odds that you’ll file a claim. And if they think that your credit isn’t up to their highest standard, they will charge you more, even if you have never had an accident, our price data show.”

“Cherry-picking about 30 of almost 130 elements in a credit report, each insurer creates a proprietary score that’s very different from the FICO score you might be familiar with, so that one can’t be used to guess the other reliably.”

“And your credit score could have more of an impact on your premium price than any other factor. For our single drivers in Kansas, for instance, one moving violation would increase their premium by $122 per year, on average. But a score that was considered just good would boost it by $233, even if they had a flawless driving record. A poor credit score could add $1,301 to their premium, on average.”

The Consumer Reports article provided data from each state that could be called up by clicking on a map.  The reader might find it enlightening.  O’Neil chose to quote data from Florida as being particularly outrageous. One could hardly disagree, so the relevant premium rates will be transcribed here.

excellent credit                        $1,409             $0
good credit                              $1,721             $312
poor credit                               $3,826             $2,417
excellent credit +DWI             $2,274             $866

The first column presents the rate for each credit score, the second is the deviation from the best rate.  Note that a person with a poor credit score and an unblemished driving record could pay $1,552 more than a person with a good credit score and a conviction for driving while intoxicated.

So much for insurance companies trying to allocate risk properly based on driving habits.  Who are the people with the poor credit scores who are hurt by this system?  Among them are some who are indeed a risk to insure, but also among them are those who are the long-term poor, those who might have lost a job temporarily and fell behind on debt payments, or those who suffered from severe health issues.  Is it good for society for those latter classes of individuals to be punished for their misfortune?  Why do automobile companies do what they do?  O’Neil provides her opinion.

“….I would argue that the chief reason has to do with profits.  If an insurer has a system that can pull in an extra $1,552 a year from a driver with a clean record, why change it?  The victims of their WMD [Weapon of Math Destruction], as we’ve seen elsewhere, are likely to be poor and less educated, a good number of them immigrants.  They’re less likely to know that they’re being ripped off.”

“In short, while an e-score might not correlate with safe driving, it does create a lucrative pool of vulnerable drivers.  Many of them are desperate to drive—their jobs depend on it.  Overcharging them is good for the bottom line.”

O’Neil then proceeds to tell her readers that that there is even more to this sordid affair: price optimization algorithms.

“….consider the price optimization algorithm at Allstate, the insurer self-branded as ‘the Good Hands People.’  According to a whatchdog group, the Consumer Federation of America [CFA], Allstate analyzes consumer and demographic data to determine the likelihood that customers will shop for lower prices.  If they aren’t likely to, it makes sense to charge them more.  And that’s just what Allstate does.”

“It gets worse.  In a filing to the Wisconsin Department of Insurance, the CFA listed one hundred thousand microsegments in Allstate’s pricing schemes.  These pricing tiers are based on how much each group can be expected to pay.  Consequently, some receive discounts of up to 90 percent off the average rate, while others face an increase of 800 percent.  ‘Allstate’s insurance pricing has become untethered from the rules of risk-based premiums and from the rule of law,’ said J. Robert Hunter, CFA’s director of insurance and the former Texas insurance commissioner.”

The Consumer Reports article should be read by all.  Not all insurers have the same practices and shopping around can provide a big difference in premiums.

O’Neil provides another troubling example where supposedly good intentions have been undermined by the discovery of a new way to extract money from an unwary group: employees with company-provided healthcare and placed in wellness programs. 

“Employers, which have long been nickel and diming workers to lower their costs, now have a new tactic to combat those growing costs.  They call it ‘wellness.’  It involves growing surveillance, including lots of data pouring in from the Internet of Things—the Fitbits, Apple watches, and other sensors that relay updates on how our bodies are functioning.”

“The idea, as we’ve seen so many times, springs from good intentions.  In fact, it is encouraged by the government.  The Affordable Care Act, or Obamacare, invites companies to engage workers in wellness programs, and even to ‘incentivize’ health.  By law, employers can now offer rewards and assess penalties reaching as high as 50 percent of the cost of coverage.  Now, according to a study by the Rand Corporation, more than half of all organizations employing 50 people or more have wellness programs up and running, and more are joining the trend every week.”

Wellness programs are intuitively a good idea, but the surveillance required is intrusive and coercive, and the highly personal data can potentially be put to unintended uses.

“Already, companies are establishing ambitious health standards for workers and penalizing them if they come up short.  Michelin, the tire company, sets its employees goals for metrics ranging from blood pressure to glucose, cholesterol, triglycerides, and waist size.  Those who don’t reach the targets in three categories have to pay an extra $1,000 a year towards their health insurance.  The national drugstore chain CVS announced in 2013 that it would require employees to report their levels of body fat, blood sugar, blood pressure, and cholesterol—or pay $600 a year.”

The process of “incentivizing” healthcare provides employers with an algorithm by which they can tune small discounts for good behavior with large penalties for bad behavior and turn a profit if they so choose.  But why would a company do such a thing if they are already saving money because wellness is saving costs from medical bills?  O’Neil reports that studies fail to find that wellness programs offer much in the way of savings for employees.  Many are ineffective at changing long-term health behavior.  Encouraging smokers to quit smoking seems to be the one area in which success has been good.  But smokers tend to have health problems that turn up later in life when employees have either moved on to a new job or have retired and healthcare is provided by Medicare.  O’Neil provides this final word on the matter.

“A 2013 study headed by Jill Horwitz, a law professor at UCLA, rips away the movement’s economic underpinning.  Randomized studies, according to the report, ‘raise doubts’ that smokers and obese workers chalk up higher medical bills than others.  While it is true that they are more likely to suffer from health problems, they tend to come later in life, when they’re off the corporate health plan and on Medicare.  In fact, the greatest savings from wellness programs come from the penalties assessed on the workers.  In other words, like scheduling algorithms, they provide corporations with yet another tool to raid their employees’ paychecks.”

O’Neil provides us with one more thing to worry about.  All the data we are providing about our bodies and our health could one day be used against us.

“My fear goes a step further.  Once companies amass troves of data on employees’ health, what will stop them from developing health scores and wielding them to sift through job candidates?  Much of the proxy data collected, whether step counts or sleeping patterns, is not protected by law, so it would theoretically be perfectly legal.  And it would make sense.  As we’ve seen, they routinely reject applicants on the basis of credit scores and personality tests.  Health scores represent a natural—and frightening—next step.”

Beware of good intentions when dealing with for-profit entities.  Especially when you elect politicians who believe you are not worth protecting with government regulations.

The attempt to move from shared risk to individual risk has proved harmful to society.  It saves a little money for many and severely punishes others—something insurance was not intended to do.

“As insurance companies learn more about us, they will be able to pinpoint those who appear to be the riskiest customers and then either drive their rates to the stratosphere or, where legal, deny them coverage.  This is a far cry from insurance’s original purpose, which is to help society balance its risk.  In a targeted world, we no longer pay the average.  Instead, we’re saddled with anticipated costs.  Instead of smoothing out life’s bumps, insurance companies will demand payment for those bumps in advance.  This undermines the point of insurance, and the hits will fall especially hard on those who can least afford them.”

The interested reader might find the following articles informative:

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