stephenlambe

9 ms·

1.
by stephenlambe 13y ago
This was discovered automatically. That's one benefit of a machine learning-based system: you feed it a lot of data and tell it when fraud actually occurred and it adapts its rules predict fraud accordingly.
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by stephenlambe 13y ago
Not quite...an order is more likely fraudulent when it was placed at 2-4am local time. Local time for the fraudster (in Vietnam or wherever else), not local time for the site they're defrauding.
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by stephenlambe 13y ago
E-commerce fraud takes many forms. Three main types of fraud impact merchants: payment fraud, new account fraud and account takeover. We described all three recently at Sift in a blog post: http://ow.ly/oPrS2 . In the stole
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by stephenlambe 13y ago
Sift intern here. We use the time zone of the customer rather than of the website for scoring riskiness. Sorry if that wasn't clear. As for customers including birth years in their emails, you're correct that this would be counter
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by stephenlambe 13y ago
it's per capita. Total fraudulent transactions/total transactions in each country.
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by stephenlambe 13y ago
Sift Science intern here. We used the latter for the purposes of this map.
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by stephenlambe 13y ago
These results are averaged across many different types of e-commerce companies. So you're correct that a particular company shouldn't necessarily set up a rule to flag transactions occurring 3-10 mins from account creation. At Sif
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by stephenlambe 14y ago
Exactly what part of that website "portrays class and ethnic conflict"?
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by stephenlambe 14y ago
They're only processing $10M of transactions/month, so even if they were to start charging fees, that wouldn't translate into much revenue. Agree that this is a great exit for the team.
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by stephenlambe 14y ago
turns out Su actually got the Seattle office a (waterless) hot tub http://seattletimes.nwsource.com/html/technologybrierdudleys...