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American Express is testing technology that is best known for helping create deepfake videos—realistic clips of people saying things they never really said—to fight financial fraud.
In this case, the company is creating fake financial data like credit card transactions that it can feed into machine-learning algorithms to better help them spot credit card scams and other problems. The goal is to more quickly alert customers that their accounts have been compromised, before criminals have a chance to go on spending sprees.
At the heart of American Express’s research is the A.I. technology known as generative adversarial networks, or GANs, which are used to create deepfake videos. In recent years, the technology has improved to the point that it can help create convincing video clips that fool viewers.
Two years ago, for instance, University of Washington researchers used GANs to create a realistic-looking video of former President Barack Obama giving a speech that he never actually gave. More recently, MIT’s Center for Advanced Virtuality lab created a deepfake video of former President Richard Nixon giving a bogus speech about the Apollo 11 moon landing mission failing.
In the case of the fake Nixon clip, MIT researchers trained GAN software on audio clips of Nixon’s speeches so that it could learn to modify an actor’s voice to sound like the former President’s. American Express researchers, on the other hand, trained their GANs on internal data that is normally used for tasks like calculating consumer credit scores, so that the software could create its own financial data.
The goal was for the GANs to create fake transactions “that look normal,” said Dmitry Efimov, the vice president of machine learning research for American Express. Data with obvious anomalies, such as multiple purchases of toilet paper in New York City on one day, followed by a lawnmower purchase in Bakersfield, Calif., the next, would be less effective.
Efimov declined to comment about how American Express could specifically use synthetic financial data to improve fraud detection, citing the risk that criminals could use the information for their benefit. But, generally speaking, the more financial data the company has, the more it can improve its cybersecurity systems.
Other organizations that are researching using GANs to create synthetic financial data include online retailing giant Amazon. In 2018, Amazon published a paper about using the software to create synthetic e-commerce transactions so that the data could eventually be used for “product recommendation, targeting deals, and simulation of future events.”
Researchers at the University of Michigan have also published a paper about using GANs to create fake stock market orders. That information could be used to help uncover stock market manipulation schemes, explained Xintong Wang, a Ph.D. candidate in the University of Michigan’s computer science department.
Still, as American Express researchers described in a paper they presented at this year’s annual Conference on Neural Information Processing Systems, it’s difficult to evaluate how effective the GANs are at creating fake financial data.
Humans can easily look at A.I.-generated images to see if they resemble the real thing. But with financial data, the technology is so new that there are no “commonly accepted techniques” that the researchers can use to grade the software, they wrote.
The American Express researchers ended up using statistical techniques to analyze the A.I.-generated data and found that the results were good but not great. The researchers plan to refine their techniques for future research.
Ultimately, the researchers are optimistic that their work will pay off. As they described in the paper, there’s a lack of publicly available financial data that they can use to train their fraud-detection models. A.I. researchers could release their synthetic data sets to the public, which would be beneficial because other researchers could build on the work, the researchers explained in the paper. But an Amex spokesperson said the financial firm has no plans to do so.
“Customers’ personal data and privacy would be protected using this approach,” the researchers wrote.
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