The problem of online fraud, fake reviews and sock puppetry is only going to get worse, according to recent research. But there are ways to identify likely perpetrators and that’s what Sift Science aims to do.
The 8-person San Francisco startup uses machine learning to analyze user interaction with web sites and create a digital profile of who will likely perpetrate online fraud, said company co-founder Brandon Ballinger, an ex-Google(s goog) software engineer.
Here are some early findings based on the private beta of the service:
1: Fraudsters tend to be nightowls. Most fake accounts are created late at night local time: 3:00 a.m is apparently the witching hour.
2: Bad guys stick with old technology. People using Chrome on Windows XP are four times more likely to create a fake ID than the average user. (Firefox users are 50 percent more likely than average to create a faux account.)
On the other side of the same coin:
3: Fakers don’t update. An account created on Safari running on Mac OS X(s aapl) is about 30 percent less likely to be fake. Those running IE9 on Windows 7 are 33 percent less likely than average to be fake.
4: Yahoo email is big. Folks with Yahoo.com(s yhoo) email accounts are five times more likely to create a fake account than someone using Gmail.com or Comcast.com (s cmcsa) addresses.
5: Geography is key. Most traffic coming from Nigeria is fraudulent but also goes through a proxy to disguise its point of origin.
“Based on user actions, we build a model of what a normal user would do on a site versus what a fradulent user would do. We look at the time of account creation, the sequence of pages viewed. If they’re browsing around, they’re probably normal. If they set up an account and jump straight to a transaction, probably not,” Ballinger told me by phone. But then again, they’re tricky. Sift Science found that someone who opens an account, then waits an hour before transacting is 7 times more likely to be fraudulent than the average user.
The proess is similar to Google Analytics in that Sift Science creates a history of user events and comes up with a score for each user that rates the likelihood that he or she is involved in fraud, he said.
Sift Science is heavy on former Googlers: 6 employees are ex-Google engineers. Jason Tan was former CTO of BuzzLabs and Fred Sadaghiani was CTO of Teachstreet.