The reality is, computers are good at some things, humans are good at others (Remember how much effort it took google to identify cats in youtube thumbnails? Something any four year old can do?). Computers are good at sifting through large amounts of data. Great. Humans are good at detecting fraud. Combining them is best.
Peter Thiel writes about how fatal machine learning for fraud detection in his book, "Zero to One".
At Paypal, Max Levchin assembled an elite team of mathematicians to study the fraudulent transfers in detail. Then we took what we learned and wrote software to automatically identify and cancel bogus transactions in real time. But it quickly became clear that this approach wouldn't would either. After an hour or two, the thieves would catch on and change their tactics. We were dealing with an adaptive enemy and our software couldn't adapt in response.
The fraudsters adaptive evasions fooled our automatic detection algorithms, but we found that they didn't fool our human analysts as easily. So max and his engineers rewrote the software to take a hybrid approach: the computer would flag the most suspicious transactions on a well designed user interface, and human operators would make the final judgment as to their legitimacy.
> computers are good at some things, humans are good at others
"You insist that there is something a machine cannot do. If you will tell me precisely what it is that a machine cannot do, then I can always make a machine that will do just that!"
-- J. von Neumann
Computers will continue to get better at human things as we continue to get better at understanding how human things work. Look at the recent advances in deep learning. This is using only the most crude approximation of human neurons we can identify and caption images with astounding results. Google currently claims that anything that can be done in 0.1 of a second by a human, they can do as well.
Fraud detection relies heavily on unsupervised learning, and for all of history up until the last few years state of the art unsupervised learning was usually SVD + clustering or some variation on that. The current state of the art, things like deep belief networks, are able to achieve markedly superior results.
Additionally this article seems to imply that they are collected labeled data from customers which should help tremendously in modeling fraud. If even if the labels are a small sample recent advances in semi-supervised learning using deep neural nets is even greater than the advances in unsupervised learning.
While I don't disagree that historically it has been wise to include a human element in fraud detection, I don't believe there is any reason to assume that trend will continue indefinitely into the future.
Sorry about this, but i think there are some big flaws in your comment.
> You insist that there is something a machine cannot do. If you will tell me precisely what it is that a machine cannot do, then I can always make a machine that will do just that!
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Know anything about the Halting problem & NP-Hard? There are things that computers will never-ever be able to perform, even if we go quantum.
> Computers will continue to get better at human things as we continue to get better at understanding how human things work
Your argument is the basic concept of symbolism, and deep learning is one of the multiple connectionism types of learning, a whole different world in ML.
> Fraud detection relies heavily on unsupervised learning
No, i have been working in Fraud and no, it is supervised with lot of manual feedback.
> unsupervised learning was usually SVD + clustering or some variation on that. The current state of the art, things like deep belief networks, are able to achieve markedly superior results.
Sorry, No Free Lunch for learning algorithms...
> historically it has been wise to include a human element in fraud detection, I don't believe there is any reason to assume that trend will continue indefinitely into the future.
> Google currently claims that anything that can be done in 0.1 of a second by a human, they can do as well.
Okay, on a moonless night, overcast, with no lights,
a lot of fog, 200 yards away is .... Bingo, a
pretty girl, 5' 4", 34, 19, 34, blond, really sweet,
wants to be great as a wife and mommy,
about 18! Yup, been doing that for years! Try
that Google! My advantage: I have a dedicated,
autonomous, peripheral processor just for that
task!
Peter Thiel writes about how fatal machine learning for fraud detection in his book, "Zero to One".
At Paypal, Max Levchin assembled an elite team of mathematicians to study the fraudulent transfers in detail. Then we took what we learned and wrote software to automatically identify and cancel bogus transactions in real time. But it quickly became clear that this approach wouldn't would either. After an hour or two, the thieves would catch on and change their tactics. We were dealing with an adaptive enemy and our software couldn't adapt in response.
The fraudsters adaptive evasions fooled our automatic detection algorithms, but we found that they didn't fool our human analysts as easily. So max and his engineers rewrote the software to take a hybrid approach: the computer would flag the most suspicious transactions on a well designed user interface, and human operators would make the final judgment as to their legitimacy.