Ekata launches ID verification tool as coronavirus fuels surge in e-commerce
In response to fraudsters seeking to capitalize on the COVID-19 influenced surge in e-commerce, Ekata has launched a new global digital ID verification tool to combat cybercrime.
The new crime-fighting tool is called Network Score and it is a machine learning-based prediction that will enable e-commerce merchants to better differentiate legitimate customers from potential fraudsters. The Network Score is created by the Ekata Identity Engine and rates a transaction on a scale from a 0 to 1 up to three decimal points.
“The Network Score is the output of a machine learning model that has 147 different inputs,” said Vivek Kumar, senior product manager at Ekata. “We are finding the behavioral elements of how people shop and we examine how a person’s five identity elements are used. They are name, email, IP address, physical address and phone [device]. We cater to all geographies, not just North America. We have been on this journey for the last three years and the Network Score is the culmination of all that work.”
Ekata’s set of identity verification tools cover a number of industries including retail e-commerce, payments processors, online lenders and marketplaces. Its client list includes Stripe, Adyen, Microsoft, Lyft, Airbnb, Postmates and Nextdoor.
“Digital identity is really hot now because COVID-19 has pushed so many consumers online,” said David Mattei, senior analyst at Aite Group. “The current challenge in authentication is a combination of having a lot of first-time users buying things and that the phenomenon is a global trend. What is unique about Ekata is their global purview or reach. They source data from multiple sources around the world. You don’t want just a solution that is U.S.-based, especially where e-commerce is headed.”
The Ekata Identity Network sources billions of transactions from over 1,700 companies worldwide. It does not rely on blacklists or consortium aggregated data. Each month over 200 million new global transactions are added to the Network from its partner retailers, making it a real-time decisioning asset that can be leveraged by Ekata’s customers.
“In order to fight e-commerce, fraud machine learning fraud prevention platforms need to collect as many uniquely differentiating signals associated with criminal activity as it can,” said Tim Sloane, vice president of payments innovation at Mercator Advisory Group.
The challenge merchants face in combating cybercrime is that so much consumer data exists on the dark web that hackers can use to create synthetic identities that use a combination of real and fake traits. For example, the Equifax breach has exposed personally identifiable information (PII) on over 150 million consumers from the U.S., U.K. and Canada. The Marriott Hotel breach exposed PII data on over 500 million consumers worldwide.
Having all of this data available for sale enables fraudsters to use the exact same tools that crime fighting platforms are using — machine learning. Over 88% of fraud attacks generated from the U.S. during the fourth quarter of 2019 were done so using automation, according to Arkose Labs. Machines accounted for almost 86% of Russian-generated fraud attacks while in the U.K. they accounted for over 92% of attacks.