Trustev Tackles E-Commerce Identity Fraud With An Online Fingerprint Made From Social Signals And More

Trustev, a Startup Battlefield company presenting today at TC Disrupt NY 2013, has developed a product to tackle online fraud using an algorithmic system of social signals, behavioural data and transaction history to create a “digital fingerprint” that lets companies verify that you are who you say you are when you are buying something online. The problem that Trustev — based out of Cork, Ireland — is tackling is big, and growing bigger. E-commerce is booming, with sales topping $1 trillion globally in 2012 and still on the rise. But the market also has a dark undercurrent in the form of fraud — specifically around people using other people’s identities to purchase goods online. Currently ID fraud is a $20 billion problem and growing at twice the rate of the e-commerce market. Beyond the very obvious issue of costing companies a lot of money, and consumers a lot of pain, there is another issue: traditional methods for trying to stem the problem are based mainly around human vetting. Pat Phelan, founder of Trustev and its CEO, says that today 27% of all transactions online are referred to contact centers for review working out to 200 man-hours ever year. “At the moment, e-commerce is only 5% of all commerce, so as it grows you will run out of human beings to process all those transactions.” And that’s not to mention the fact that current systems are inefficient. “Verification is a major operational cost,” he adds. “You have major cost centers created out of people Googling names, and trying to figure out if people are who they say they are.” The social networks that Trustev tracks for identity signals include Facebook, Twitter and LinkedIn as defaults. In different regions, it plans to add others such as Orkut in Latin America, V-connect (from Vkontakte) in Russia, and Sina Weibo in China. Users check in to social networks when checking out to speed up the process. Like other big data plays, the idea with Trustev is that it takes the mountain of data that is presented through these channels, and parses it and matches it up with other online activity associated with a person. This can also be used to help verify in a positive way, rather than simply to note when someone is not who they say they are. A classic example is the case of a person travelling: some payment networks, when

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