{"id":647493,"date":"2013-03-19T06:30:34","date_gmt":"2013-03-19T10:30:34","guid":{"rendered":"http:\/\/gigaom.com\/?p=621578"},"modified":"2013-03-19T06:30:34","modified_gmt":"2013-03-19T10:30:34","slug":"sift-science-says-it-can-sniff-out-cyber-fraud-before-it-gets-expensive","status":"publish","type":"post","link":"https:\/\/mereja.media\/index\/647493","title":{"rendered":"Sift Science says it can sniff out cyber fraud \u2014 before it gets expensive"},"content":{"rendered":"

Sift Science<\/a>, the startup forged by a gaggle of former Googlers, is ready for its close up. As of Tuesday, the company is opening up the testing of its fraud-detection service for e-commerce and other sites to the public and has $5.5 million in funding from some heavy-hitter investors to back its play.<\/p>\n

The San Francisco company, which we covered in October<\/a>, claims it can scope out more fraudsters before they do harm because it is not constrained by the finite number of rules that most vendors use to flag suspicious activity. “Many anti-fraud technologies follow a set number, maybe 175 to 225 rules, against which to measure user behavior — \u00a0the problem is fraudsters don’t follow the rules and change all the time,” Sift Science co-founder Brandon Ballinger said in a recent interview.<\/p>\n

\"Customers<\/a><\/p>\n

Customers can flag users as fraudsters in order to train Sift Science\u2019s algorithm to spot patterns unique to their site.<\/p>\n<\/div>\n

“We take a machine learning approach to learn from patterns early as they form to predict whether a new user is fraudulent,” he said. Sift Science’s machine learning algorithm has automatically learned one million patterns that predict fraud, and as more sites join the network, it will learn more patterns to help everybody fight fraud more accurately, he said.<\/p>\n

Speeding up fraud defense<\/h2>\n

“You might expect the worst type of users to sign onto a site and make an immediate purchase [with a stolen credit card] but in reality if they wait an hour or so they’re more likely to be a fraudster than a fast buyer,” Ballinger said. “Or, if you’re an auction site and a seller posts an item where the text is all in caps, the user is four times more likely to be a fraudster — they’re not posting real items, it’s usually some sort of money laundering scheme, they’ll have 100 stolen credit cares and create a seller account and a bunch of buyer accounts then they post fake items and buy them.”<\/p>\n

But the bad guys are always going to change things up to avoid detection. An example, \u00a0in the past, the most popular time to conduct online fraud was at 3 a.m. local time but now it’s midnight to 1 a.m., Ballinger said.\u00a0And, while a large percentage of traffic coming out of Nigeria remains fraudulent, a whopping 81 percent of fraud comes from U.S.-based IP addresses. “That means either they’re in the U.S. or are smart enough to use a proxy,” Ballinger said.<\/p>\n

Applying webscale data and analytics to fraud detection<\/h2>\n

That fluidity and flexibility is important as is the company’s Google DNA. Ballinger said 8 of the company’s 9 employees are engineers and 5 are ex-Google engineers. “We’re taking the Google approach of large-scale machine learning,” he said. Except Sift Science is running on Hadoop, Hbase and MapReduce on Amazon’s public cloud.<\/p>\n

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Network funneling is one of many fraud patterns identified by Sift Science, which uses symmetry to detect when a fraudster is funneling money through a large network of accounts.<\/p>\n<\/div>\n

It is thus able to use Amazon Web Services huge scale –and the network effect of all the companies on it — to build its knowledge base. “If someone attacks AirBNB and Affirm we can apply that knowledge and use it elsewhere,” Ballinger said.<\/p>\n

Sift Science, which relies on what Ballinger calls a dead-simple REST API, \u00a0will face off against in the fraud detection space including\u00a0Silver Tail Systems<\/a>, which EMC bought last year<\/a>\u00a0and which watches and tracks user navigation trends; and Threatmetrix<\/a> which watches device IDs.<\/p>\n

Early customers\u00a0include payment processors, online retailers, and some name brands including\u00a0Airbnb<\/a>\u00a0and\u00a0Affirm<\/a>,\u00a0Max Levchin’s latest startup.<\/a><\/p>\n

The $5.5 million in funding comes from some big names including Union Square Ventures; Max Levchin of PayPal, Slide and Affirm fame; Marc Benioff of Salesforce.com; Kevin Scott of AdMob, Google\u00a0and LinkedIn; Alexis Ohanian (Reddit and\u00a0YCombinator); and Rich Barton (Zillow and.)<\/p>\n

Prospective customers can sign up on Sift Science’s site<\/a> for the service, which is free of charge for\u00a0their first 5,000 users, the service is free; after that it’s 10 cents per user.<\/p>\n

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