New crime needs new algorithms

How PayPal uses deep learning and detective work to fight fraud

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Hui Wang has seen the nature of online fraud change a lot in the 11 years she’s been at PayPal. In fact, a continuous evolution of methods is kind of the nature of cybercrime. As the good guys catch onto one approach, the bad guys try to avoid detection by using another.

Today, said Wang, PayPal’s senior director of global risk sciences, “The fraudsters we’re interacting with are… very unique and very innovative. …Our fraud problem is a lot more complex than anyone can think of.”

In deep learning, though, Wang and her team might have found a way to help level the playing field between PayPal and criminals who want exploit the online payment platform.

Deep learning is a somewhat new approach to machine learning and artificial intelligence that has caught fire over the past few years thanks to companies such as [company]Google[/company], [company]Facebook[/company], [company]Microsoft[/company] and Baidu, and a handful of prominent researchers (some of whom now work for those companies). The field draws a lot of comparisons to the workings of the human brain because deep learning systems use artificial neural network algorithms, although “inspired by the brain” might be a more accurate description than “modeled after the brain.”

How DeepFace sees Calista Flockhart. Source: Facebook
A visual diagram of a deep neural network for facial recognition. Source: Facebook

Essentially, the stacks of neural networks that comprise deep learning models are very good at recognizing patterns and features of the data they’re trained on, which has led to some huge advances in computer vision, speech recognition, text analysis, machine listening and even video-game playing in the past few years. You can learn more about the field at our Structure Data conference later this month, which includes deep learning and artificial intelligence experts from Facebook, Microsoft, Yahoo, Enlitic and other companies.

It turns out deep learning models are also good at identifying the complex patterns and characteristics of cybercrime and online fraud. Machine-learning-based pattern recognition has long been a major part of fraud detection practices, but Wang said PayPal has seen a “major leap forward” in its abilities since it began investigating precursor (what she calls “non-linear”) techniques to deep learning several years ago. PayPal has been working with deep learning itself for the past two or three years, she said.

Some of these efforts are already running in production as part of the company’s anti-fraud systems, often in conjunction with human experts in what Wang describes as a “detective-like methodology.” The deep learning algorithms are able to analyze potentially tens of thousands of latent features (time signals, actors and geographic location are some easy examples) that might make up a particular type of fraud, and are even able to detect “sub modus operandi,” or different variants of the same scheme, she said.

Some of PayPal's fraud-management options for developers.
Some of PayPal’s fraud-management options for developers.

The patterns are much more complex than “If someone does X, then the result is Y,” so it takes artificial intelligence to analyze them at a level much deeper than humans can. “Actually,” Wang said, “that’s the beauty of deep learning.”

Once the models detect possible fraud, human “detectives” can get to work assessing what’s real, what’s not and what to do next.

PayPal uses a champions-and-challengers approach to deciding which fraud-detection models to rely on most heavily, and deep learning is very close to becoming the champion. “We’ve seen roughly a 10 percent delta on top of today’s champion,” Wang said, which is very significant.

And as the fraudulent behavior on PayPal’s platform continues to grow more complex, she’s hopeful deep learning will give her team the ability to adapt to these new patterns faster than before. It’s possible, for example, that PayPal might some day be able to deploy models that take live data from its system and become smarter, by retraining themselves, in real time.

“We’re doing that to a certain degree,” Wang said, “but I think there’s still more to be done.”

3 Responses to “How PayPal uses deep learning and detective work to fight fraud”

  1. Jan Bilek

    This article sounds cool and sciencey but from what I can observe as an outsider, the real algorithm they use is something like “if you suddenly get more money, we will block your account and leave your money inaccessible for months with no real chance to do anything about it”. There are numerous stories of people who got their accounts blocked after successfully funding their campaign via Kickstarter or something similar. Some of them even with “we will fix that” promises from PayPal top-people – but this is only when some poor schmuck who got his account blocked somehow managed to get his rant to the main page of Hacker News and PayPal is trying to reduce the PR damage. I personally stopped using PayPal for exactly this reason and it would take a lot more than this article to gain my trust.

  2. dennis

    So how deep are they going, it seems more innocent victims are caught up in this anti fraud stuff , i know someone works for paypal and the amount of Identification theft is Huge. and yet paypal customer service acts like robots and limits accounts forever. never sign on to someones computer if they have paypal and ebay your both accounts will be Nuked, and never sign on mobile device you’ll be nuked

    it was even on watchdog about paypal peoples accounts being nuked for no reason

  3. Peyton

    Yea…PayPal deep learning sucks. I have not had it work one time without calling them to turn down the fraud alarm. M-F 8-5 when they are taking calls btw.