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Hi there! A lot of the fraud detection we do is actually anomaly detection, where we're not training a classifier on a labeled dataset, but instead creating a m
by vonnik 10y ago
Hi there! A lot of the fraud detection we do is actually anomaly detection, where we're not training a classifier on a labeled dataset, but instead creating a model that can surface unusual behavior. The subset of data that we surface is then passed to human analysts. What they care about, generally, is a low false positive rate, because false positives waste their time. We talk about this a bit more here: https://skymind.io/case-studies/orange https://skymind.io/case-studies/orange Sorry if that doesn't quite answer your question.
- yingxie3 10y agoFor fraud detection such as what you did for orange, is there a rule of thumb on how much data one would need? Before heading down the path I would like to know whether my problem is solvable given the amount of data I have. Thanks in advance!
- agibsonccc 10y agoWe were in the terabytes. A viable proof of concept could be built with a few gigs of transactions to validate the idea though. The main bit with orange that isn't in the marketing material: We used unsupervised methods for this, not supervised.