Credit card fraud costs US merchants and credit card companies more than $3.4 billion a year. That figure would undoubtedly be much higher without the use of computer surveillance systems to monitor every transaction.
One of the most proven antifraud systems is Falcon Fraud Manager, which keeps tabs on more than 4 billion transactions a month and uses lightning fast neural networks to scan for suspicious purchase patterns.
Neural networks were originally designed to mimic human gray matter. Over time, however, the technology has moved far beyond brain simulation to become a basic building block of many computer systems capable of learning and pattern recognition.
The networks typically consist of layers of interconnected neurons, each of which produces a signal only when its input exceeds a certain threshold. Though the individual neutrons are simple, the net as a whole can learn to recognize complex patterns of inputs.
The system specializes in detecting things a human would never notice. For example, if you use your card to buy a tank of gas and then go directly to a jewelry store to make a purchase, your account will almost surely be flagged, especially if you’re not a person who buys a lot of bling.
The reason: over years of correlating variables, testing, and learning, the system has noticed that a criminal’s first stop after stealing a credit card is often a gas station. If that transaction goes through, the thief knows the card hasn’t been reported stolen as yet and heads off on a spending spree, often at some high-priced retailer.