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Experian Enlists Behavioral Biometrics Startup to Combat Online Fraud

April 7, 2017, 10:37 PM UTC
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LONDON - OCTOBER 14: A fingerprint scanner is demonstrated during the Biometrics 2004 exhibition and conference October 14, 2004 in London. The conference will examine the role of new technology such as facial recognition and retinal scans to determine identity to improve security. (Photo by Ian Waldie/Getty Images) *** Local Caption ***
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Credit bureau Experian has joined forces with technology firm BioCatch to use behavioral biometrics to help its clients spot fraudsters applying for credit cards and other lending products online, the companies said on Friday.

BioCatch’s software, which was integrated in Experian’s fraud prevention platform, analyzes the way users interact with devices and websites by tracking a wide range of factors including how fast they type or the way they move their mouse across a web page. The information collected can help BioCatch determine whether users who are who they say they are or impostors.

The companies said behavioral biometrics is especially useful in spotting when automated programs, or bots, are being used by fraudsters to apply for credit products online by using stolen identity information they acquire on the “dark net,” an area of the internet only accessible with special browsers that can ensure anonymity.

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“Behavioral biometrics is one of the up and coming techniques for defending against fraud,” said John Sarreal, director of product management at Experian (EXPGY). “It is effective in this day and age when you have more scripted attacks and more bot armies recruited to submit applications.”

Financial institutions are seeking to deploy more sophisticated methods to counter the rising threat of online fraud and cyber crime. According to analyst firm Javelin, roughly 6 percent of consumers in the United States were victims of identify fraud in 2016, an increase of more than 2 million victims from the previous year.

Frances Zelazny, vice president of BioCatch, said several behaviors can be monitored to distinguish between fraudsters and normal users, such as how familiar the user is with the application process.

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For example, fake users might be slower than real ones in filling out basic information like date of birth and name but might be very familiar with how a form is structured and will move more quickly between questions.

For real users, “there tend to be natural pauses between data entry, as you don’t really know if the form is going to ask you for your phone number, or zip code next,” Zelazny said.