The SuperFreakonomics Virtual Book Club invites readers to ask questions of some of the researchers and other characters in our book. (Earlier Q&A’s can be found here.)
This week we’re offering up “Ian Horsley.” By day, he is employed in the anti-fraud department of a large British bank; but in his every spare moment for the past few years he has been working hard in collaboration with Steve Levitt to build an algorithm that can identify potential terrorists by their retail banking data. A few excerpts:
He doesn’t work in law enforcement, or in government or the military, nor does anything in his background or manner suggest he might be the least bit heroic. He grew up in the heart of England, the son of an electrical engineer, and is now well into middle age. He still lives happily far from the maddening thrum of London. While perfectly affable, he isn’t outgoing or jolly by any measure; Horsley is, in his own words, “completely average and utterly forgettable.”
And:
The procedure [to build the algorithm] would require two steps. First, assemble all the available data on these hundred-plus suspects [already arrested by British police after the 7/7 bombings] and create an algorithm based on the patterns that set these men apart from the general population. Once the algorithm was successfully fine-tuned, it could be used to dredge through the bank’s database to identify other potential bad guys. Given that the United Kingdom was battling Islamic fundamentalists and no longer, for instance, Irish militants, the arrested suspects invariably had Muslim names. This would turn out to be one of the strongest demographic markers for the algorithm.
And:
There were also some prominent negative indicators. The data showed that a would-be terrorist was disproportionately unlikely to:
Have a savings account
Withdraw money from an ATM on a Friday afternoon
Buy life insuranceThe 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 an insurance company may not pay out if the policyholder commits a suicide bombing. So a twenty-six-year-old family man who suspects he may one day blow himself up may not waste money on life insurance.
And:
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.
and 
. You can also watch it on 
Well, did you ever get any feedback? Does your “secret sauce” work?
Dismal science if ever there was.
If these people get arrested and tried then we hear about it. They do not have to actually blow anything up.
False positives? Any mass screening program like for prostate or breast cancer throws up so many false positives you have to be careful how you apply them. Similarly the security theatre at ariports.
So what is the false positive rate of this little fable then?
Has the technique been applied to an independent data set which wasn’t used for the curve fitting and for which we have a good idea of the results? Eg, you could deliberately withhold the data of 2 known terrorists in the initial analysis and then see how they are subsequently classified.
In short is there any actual evidence for the effectiveness of this egregious piece of armchair anti-terrorism?
I like your books, just wanted to say that right of. I enjoy this site, and many things that deal with incentives. I am an Organizational Psych (OP) major, and think that there are some things that your approach can be applied to in the field, also something you might find interesting about OP.
I was wondering about incentives. In a lot of ways incentives create motivation and influence perception. However, I was wondering about how do you figure out incentives? Sure money is a big one, and some other resources relating to currency, but are incentives cultural in your opinion or are they general for all humans? Are some incentives for instance camoflauged to appear as one thing but are in reality another? I know instinctively the answers to most of these questions, as a general non-economist, but what does an economist think of these questions? and how would a freak-conomist answer them? Hopefully interesting.
Another intersting thing about incentives I find is purchases that have no basis other than appeal, iphone being a case. In most cases one doesn’t need such a phone, yet there are millions among millions who feel that they do. What are your opinions on the matter? What is the incentive? and Why do we want them so badly? from a freakonomics perspective if you can please~
I really enjoyed this part of the book. Although a little creepy, I really enjoy profiling exercises. I like how human nature often betrays itself in ways we don’t typically realize.
If I remember correctly, Ian obtained his job sort of by accident. How would one go about getting a job like his? What would Ian advise I do, to get a job similar to his or helping him?
Obvious problem:
Knowing this, now any potential terrorist reading this will be duty bound to withdraw funds from an ATM on Fridays, get a savings account, and purchase life insurance.
although, purchasing new underwear….if may see that in your data…may be quite signficant when correlated with other data points. but don’t tell anyone about that point.
Can you get much unique information about people from bank records, which isn’t already present in other data that law enforcement have access to? For instance, suppose that the British police had a long list of potential terrorism suspects, created from other data sources, and already knew that they were all observant Muslims. Would the bank data help to narrow this list down to a much smaller list of suspects who were especially likely to be terrorists, or would it mostly just be redundant with what the police already knew?
I’m an American. I have life insurnace through two different companies (one private and the other through my employer). I have several savings accounts with one bank and an investment account with an online brokerage.
What bothers me about this post is that there is supposedly a single database that not only contains all this information but also can quickly see whether or not I use my ATM card on Fridays! While there are obvious privacy concerns, a more practical concern is that such an algorithm seems to assume that we have access to such universal data. Do we?
Wow, this is really chilling. How do you define a “potential terrorist?” Angry? Anti-American? Pro-violence? Well, you could say that about just about any Tea-Party person these days.
This is the problem with most statisticians I hear these days. They look at numbers and see “reality”. But what they really see justifications for their prejudices.