The Green Sheet Online Edition
September 26, 2011 • Issue 11:09:02
Is self learning the next step in the fraud fight?
In May 2011, Michaels Stores Inc. reported a network breach that involved about 90 tampered PIN pads in retail locations across 20 states and resulted in the compromise of tens of thousands of cardholder accounts. Cards had been skimmed on the tampered terminals as far back as December 2010.
According to Mike Alford, Managing Director of U.K.-based fraud prevention specialist Alaric International, the Michaels breach would have been detected earlier if the card issuing banks involved had used Alaric's self-learning fraud detection model, called Fractals (short for Fraud Risk Analysis - Creation, Testing and Learning System).
"One of the big advantages of self learning for card issuers is that it greatly increases the chance of stopping a fraud run early, often on the first fraudulent transaction in a run on the card," Alford said. "So the answer is yes, self learning would have enabled card issuers to catch a Michaels-style fraud very early."
Train the artificial brain
In a July 2011 white paper, Alford said the fraud detection rate rises from around 70 percent for a conventional model to 85 to 90 percent using Alaric's self-learning model. According to Alford, a typical fraud model might catch about 40 percent of fraud by recognizing that first transaction is a fraudulent one, while Fractals would catch that first fraudulent transaction approximately 65 percent of the time.
Fractals is more agile and proficient at detecting fraud because of the way the self-learning model is initially "trained," Alford noted. "You take maybe three months of data or six months of data for a given financial institution," he said. "That data will have had fraud transaction marks in it. And what you do is you train the model on that data so that it learns from the data so that it can predict future fraud."
It is that ability to self-adjust as fraud changes that sets Fractals apart, according to Alford. Using Fractals, no two issuing bank's fraud systems are the same, as the model adapts to the institution's specific fraud patterns.
Additionally, a typical fraud detection system has to be taken off line to be retrained, which could last several days, Alford said. Fractals is deployed through a point-and-click user interface that allows rules to be "set up, tested and deployed in something like three to four minutes without any programming, coding or scripting being required," he added.
Banks have indicated that, as a consequence to the Durbin Amendment to the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010, they will be shorted debit card interchange revenues they could reinvest in fraud prevention. Alford said Alaric's system would allow financial institutions to maintain their anti-fraud standards in a post-Durbin environment.
"Self learning delivers a major detection performance boost but is delivered as a part of our normal competitively priced Fractals offering. ... It is not expensive to implement self learning," Alford said.
The prize of artificial intelligence
Kent Poulson, Chief Operating Officer at American Fork, Utah-based Chargeback Guardian Inc, questioned whether self learning is any different from other fraud detection methods when it comes to major shifts in fraud. "The problem you run into is, now that you've got this self learning, what if there's all of a sudden a change in the market?" he said. "Now you've got all these things that are happening. ... Now you have to reset it and start from zero again if you have a major shift."
Alford responded, saying, "In general, our self-learning approach will recognize emerging fraud patterns unless the new pattern is somehow a strong function of some new variable which has not been used when the model was initially built." In case a Fractals model needs to be retrained, the process is done remotely by Alaric and typically takes a "few hours," he added.
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