[Infowarrior] - How Visa Predicts Divorce

[Richard Forno]

(c/o JH)

How Visa Predicts Divorce
by Nicholas Ciarelli

http://www.thedailybeast.com/blogs-and-stories/2010-04-06/how-mastercard-predicts-divorce/full/

By scrutinizing your purchases, credit companies try to figure out if  
your life is about to change—so they’ll know what to sell you.
If you ever doubted the power of the credit card companies, consider  
this: Visa, the world’s largest credit card network, can predict how  
likely you are to get a divorce. There’s no consumer-protection  
legislation for that.

Why would Visa care that your marriage is on the rocks? Yale Law  
School Professor Ian Ayres, who included the Visa example in his book  
Super Crunchers, says “credit card companies don't really care about  
divorce in and of itself—they care whether you're going to pay your  
card off." And because people who are going through a divorce are more  
likely to miss payments, your domestic troubles are of great interest  
to a company that thrives on risk management. Exactly how the credit  
industry does it—through sophisticated data-mining techniques—is a  
closely guarded secret. (Visa did not return requests for comment.)

The mobile social network Loopt or its competitors could conceivably  
predict with 90 percent accuracy where an individual will be tomorrow.

Predicting people’s behavior is becoming big business—and increasingly  
feasible in an era defined by accessible information. Data crunching  
by Canadian Tire, for instance, recently enabled the retailer's credit  
card business to create psychological profiles of its cardholders that  
were built upon alarmingly precise correlations. Their findings:  
Cardholders who purchased carbon-monoxide detectors, premium birdseed,  
and felt pads for the bottoms of their chair legs rarely missed a  
payment. On the other hand, those who bought cheap motor oil and  
visited a Montreal pool bar called "Sharx" were a higher risk. "If you  
show us what you buy, we can tell you who you are, maybe even better  
than you know yourself," a former Canadian Tire exec said.

Credit card companies have also used predictive modeling to answer  
questions such as, has this cardholder recently moved? "There's a  
whole market out there that has tried to predict whether someone has  
just moved, and to be first with offers," says Bob Grossman, director  
of the Laboratory for Advanced Computing at the University of Illinois  
at Chicago. "Those kinds of things tend to be pretty high value." If a  
credit card issuer can quickly determine that a cardholder has moved,  
then the issuer's marketing partners—a home refurb business, for  
instance—can be the first to swoop in.

Last year, American Express began offering select cardholders $300  
simply to close their accounts and walk away—individuals who the  
company clearly felt were too much of a risk to keep on its books. And  
the factors that go into such a calculation have become considerably  
more sophisticated than the simple matter of whether cardholders have  
paid their bills on time.

The credit card industry is just an early adopter of a number- 
crunching game that’s increasingly transforming businesses from  
airlines to gambling. "Thirty years ago, loan officers used to look  
you in the eye and tell you whether you were the right kind of person  
to trust for a loan. That was a really inaccurate approach. Just using  
FICO scores did a much better job," Ayres says. "Credit card companies  
started using a similar approach in deciding whether to issue and how  
to price their card. It's getting to be a more nuanced statistical  
game."

Other industries have bolstered their bottom lines by predicting how  
consumers will behave, according to Super Crunchers. UPS predicts when  
customers are at risk of fleeing to one of its competitors, and then  
tries to prevent the loss with a telephone call from a salesperson.  
And with its “Total Rewards” card, Harrah’s casinos track everything  
that players win and lose, in real time, and then analyze their  
demographic information to calculate their “pain point”—the maximum  
amount of money they’re likely to be willing to lose and still come  
back to the casino in the future. Players who get too close to their  
pain point are likely to be offered a free dinner that gets them off  
the casino floor.

The statistical guessing game is also becoming one that consumers can  
play. For example, the New York-based startup Hunch offers  
personalized recommendations after users answer a series of questions  
that give the site a sense of their tastes. Do you live in the  
suburbs? Do you like bumper cars? Are you more likely to spoon or be  
spooned? Out of this examination, Hunch generates a “taste profile”  
for each of its users.

Hunch then looks for statistical correlations between the information  
that all of its users provide, revealing fascinating links between  
people’s seemingly unrelated preferences. For instance, Hunch has  
revealed that people who enjoy dancing are more apt to want to buy a  
Mac, that people who like The Count on Sesame Street tend to support  
legalizing marijuana, that pug owners are often fans of The Shawshank  
Redemption, and that users who prefer aisle seats on planes "spend  
more money on other people than themselves."

Through “machine learning,” the Hunch algorithm is developing a sense  
of what individuals with a certain taste profile will prefer—a sense  
that is being improved with each new user of the Web site. This  
knowledge then allows the system to make predictions of what an  
individual user might like: a movie soundtrack, a cat name, a  
restaurant in Los Angeles. Kelly Ford, the startup's vice president of  
marketing, notes that while the credit card companies rely on a small  
set of inputs to make predictions, Hunch's questions collect "nearly  
unlimited aspects of who you are and how and what you think."

As new sets of data are collected about our lives, that data will  
contain a new set of predictions about us, waiting to be mined. The  
question will be how much control we have over that process. At the  
South by Southwest Interactive conference in March, Sam Altman, chief  
executive of the mobile social network Loopt, said that by using the  
available data, Loopt or its competitors could conceivably predict  
with 90 percent accuracy where an individual will be tomorrow.

He said hedge funds have contacted Loopt to try to purchase its data  
set so that they can forecast how much traffic a particular store will  
get. The startup declined. It doesn’t take much predictive prowess to  
see that these issues will become major matters of contention in the  
years to come.

Correction: The headline of this article originally referenced  
MasterCard, not Visa.

Nicholas Ciarelli is the former publisher of Think Secret, an Apple  
news Web site. He currently works on the product team at The Daily  
Beast.