Secrets of the digital detectives
Sep 21st 2006 - From The Economist print edition
Computing: How fraud-detection systems combine dozens of clues to spot suspicious patterns in mountains of transactions
THE pleasure of reading a classic detective story comes from the way that the sleuth puts together several clues to arrive at a surprising conclusion. What is enjoyable is not so much finding out who the villain is, but hearing the detectives explain their reasoning. Today, not all detectives are human. At insurance companies, banks and telecoms firms, fraud-detection software is used to comb through millions of transactions, looking for patterns and spotting fraudulent activity far more quickly and accurately than any human could. But like human detectives, these software sleuths follow logical rules and combine disparate pieces of data—and there is something curiously fascinating about the way they work.
Consider car insurance. Every Monday morning, telephone operators at insurance firms listen to stories of the weekend's motoring mishaps, typing the answers to several dozen standard questions into their computers. Once, each claim form then passed to a loss adjuster for approval; now software is increasingly used instead. The Monday-morning insurance claims, it turns out, are slightly more likely to be fraudulent than Tuesday claims, since weekends make it easier for policyholders who stage accidents to assemble friends as false witnesses. A single rule like that is straightforward enough for a human loss adjuster to take into account. But fraud-detection software can consider dozens of other variables, too.
If a claimant was nearly injured (because of an impact near the driver's seat, for example), the accident is less likely to have been staged and the claim less likely to be fraudulent, even if it is being filed on a Monday. Drivers of cars with low resale values are proportionately more likely to file fraudulent claims. But that factor is less important if the claimant also owns a luxury car, which suggests affluence. And if the insurance on the luxury car has expired, the likelihood of foul play drops further, since this increases the likelihood a person will drive a cheaper but properly insured car. And so on.
For more:
http://www.economist.com/science/tq/displaystory.cfm?story_id=7904281
Computing: How fraud-detection systems combine dozens of clues to spot suspicious patterns in mountains of transactions
THE pleasure of reading a classic detective story comes from the way that the sleuth puts together several clues to arrive at a surprising conclusion. What is enjoyable is not so much finding out who the villain is, but hearing the detectives explain their reasoning. Today, not all detectives are human. At insurance companies, banks and telecoms firms, fraud-detection software is used to comb through millions of transactions, looking for patterns and spotting fraudulent activity far more quickly and accurately than any human could. But like human detectives, these software sleuths follow logical rules and combine disparate pieces of data—and there is something curiously fascinating about the way they work.
Consider car insurance. Every Monday morning, telephone operators at insurance firms listen to stories of the weekend's motoring mishaps, typing the answers to several dozen standard questions into their computers. Once, each claim form then passed to a loss adjuster for approval; now software is increasingly used instead. The Monday-morning insurance claims, it turns out, are slightly more likely to be fraudulent than Tuesday claims, since weekends make it easier for policyholders who stage accidents to assemble friends as false witnesses. A single rule like that is straightforward enough for a human loss adjuster to take into account. But fraud-detection software can consider dozens of other variables, too.
If a claimant was nearly injured (because of an impact near the driver's seat, for example), the accident is less likely to have been staged and the claim less likely to be fraudulent, even if it is being filed on a Monday. Drivers of cars with low resale values are proportionately more likely to file fraudulent claims. But that factor is less important if the claimant also owns a luxury car, which suggests affluence. And if the insurance on the luxury car has expired, the likelihood of foul play drops further, since this increases the likelihood a person will drive a cheaper but properly insured car. And so on.
For more:
http://www.economist.com/science/tq/displaystory.cfm?story_id=7904281
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