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hi suprgeek, I'm the op (and CEO of Sift Science). it's true - we do not have any rules in our product. we believe that rules can serve as an effective short-te

hi suprgeek, I'm the op (and CEO of Sift Science). it's true - we do not have any rules in our product. we believe that rules can serve as an effective short-term solution, or for special cases, e.g. prematurely blocking a massive wave of fraud from an IP address because you know it's coming. but, rules are rather easy for fraudsters to circumvent, and they require merchants to play whack-a-mole. with today's technologies, it's easier than ever to analyze massive amounts of data, and we believe that machine learning can go a really long way in detecting fraud. does that make sense? happy to discuss further, and we'd be happy to put you in touch with our customers if you'd like to hear more about our results.

suprgeek 12y ago
Hi Jason, Thanks for replying and that (no rules just data) is a pretty cool ML + Big Data play. Having worked in this space a bit, I know where the dragons be in such an approach. I imagine that you are doing some kind of Pattern Matching: e.g. known Fraudster uses these signals (Browser +OS + Email+Time of shopping + something else... + type of Card) to teach the system what to look for. The real trick is to avoid over training and evolving the patterns to keep pace with the fraudsters by incorporating feedback from the merchants. Can you point to some blog posts/text that provides a sneak peak into the kinds of technology that you use to cut down on false positives?