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There were also some prominent negative indicators. The data showed that a would-be terrorist was disproportionately unlikely to:

Have a savings accountWithdraw money from an ATM on a Friday afternoonBuy life insurance

The no-ATM-on-Friday metric would seem to be a proxy for a Muslim who attends that day’s mandatory prayer service. The life-insurance marker is a bit more interesting. Let’s say you’re a twenty-six-year-old man, married with two young children. It probably makes sense to buy some life insurance so your family can survive if you happen to die young. But insurance companies don’t pay out if the policyholder commits suicide. So a twenty-six-year-old family man who suspects he may one day blow himself up probably isn’t going to waste money on life insurance.

This all suggests that if a budding terrorist wants to cover his tracks, he should go down to the bank and change the name on his account to something very un-Muslim (Ian, perhaps). It also wouldn’t hurt to buy some life insurance. Horsley’s own bank offers starter policies for just a few quid per month.

All these metrics, once combined, did a pretty good job of creating an algorithm that could distill the bank’s entire customer base into a relatively small group of potential terrorists.

It was a tight net but not yet tight enough. What finally made it work was one last metric that dramatically sharpened the algorithm. In the interest of national security, we have been asked to not disclose the particulars; we’ll call it Variable X.

What makes Variable X so special? For one, it is a behavioral metric, not a demographic one. The dream of anti-terrorist authorities everywhere is to somehow become a fly on the wall in a roomful of terrorists. In one small, important way, Variable X accomplishes that. Unlike most other metrics in the algorithm, which produce a yes or no answer, Variable X measures the intensity of a particular banking activity. While not unusual in low intensities among the general population, this behavior occurs in high intensities much more frequently among those who have other terrorist markers.

This ultimately gave the algorithm great predictive power. Starting with a database of millions of bank customers, Horsley was able to generate a list of about 30 highly suspicious individuals. According to his rather conservative estimate, at least 5 of those 30 are almost certainly involved in terrorist activities. Five out of 30 isn’t perfect—the algorithm misses many terrorists and still falsely identifies some innocents—but it sure beats 495 out of 500,495.

As of this writing, Horsley has handed off the list of 30 to his superiors, who in turn have handed it off to the proper authorities. Horsley has done his work; now it is time for them to do theirs. Given the nature of the problem, Horsley may never know for certain if he was successful. And you, the reader, are even less likely to see direct evidence of his success because it would be invisible, manifesting itself in terrorist attacks that never happen.

But perhaps you’ll find yourself in a British pub some distant day, one stool away from an unassuming, slightly standoffish stranger. You have a pint with him, and then another and a third. With his tongue loosened a bit, he mentions, almost sheepishly, that he has recently gained an honorific: he is now known as Sir Ian Horsley. He’s not at liberty to discuss the deeds that led to his knighthood, but it has something to do with protecting civil society from those who would do it great harm. You thank him profusely for the great service he has performed, and buy him another pint, and then a few more. When the pub at last closes, the two of you stumble outside. And then, just as he is about to set off on foot down a darkened lane, you think of a very small way to repay his service. You push him back onto the curb, hail a taxi, and stuff him inside. Because, remember, friends don’t let friends walk drunk.

SuperFreakonomics

SuperFreakonomics

CHAPTER 3

UNBELIEVABLE