CA Applies Real-Time Behavioral Analytics and Machine Learning to
As a cloud-based service, CA Risk Analytics Network incorporates a new advanced neural network model, backed by real-time machine learning, to protect 3-D Secure card-not-present (CNP) transactions. It learns from, and adapts to, suspected fraudulent transactions in an average of five milliseconds, instantly closing the gap for potential fraud using the same card or device across all members of the network.
According to Javelin's 2017 Identity Fraud Report, explosive growth in card-not-present fraud, driven by the increasing e-commerce and m-commerce volume, as well as the EMV liability shift, contributed to the rise of existing-card fraud. "Just as e-commerce is displacing point-of-sale transactions, the same is true for the channels in which fraudsters choose to conduct their business. Among consumers, there was a 42 percent increase in those who had their cards misused in a CNP transaction in 2016, compared to 2015 levels," the report showed.
"Detecting anomalies quickly and ensuring frictionless authentication
are the first steps in preventing card-not-present fraud without
impacting legitimate cardholder transactions," said
CA's payment security solutions protect billions of online transactions supporting hundreds of millions of cards and thousands of card portfolios worldwide. CA Risk Analytics Network is open to card issuers with portfolios of any size: from global banks with millions of cardholders, to smaller, or regional financial institutions.
Support for 3-D Secure protocols today and in the future
CA Risk Analytics Network and the CA Payment Security Suite support the 3-D Secure specification today, and will support the new EMV 3-D Secure 2.0 specification, which addresses authentication and security for card-not-present, e-commerce transactions using smart phones, mobile apps, digital wallets and other forms of digital payment. The 2.0 protocol will make extensive use of device data, giving CA Risk Analytics Network subscribers a growing new source of information to reduce fraud and optimize the customer experience across all consumer shopping devices and all versions of the 3-D Secure protocol. Support for both the 1.0 and 2.0 specifications is important as adoption rates of the updated specification among card issuers and merchants will vary.
*Data based on applying the new CA Risk Analytics Network fraud model to historical customer data over a 90-day period.
** Potential savings based on existing-card account fraud of
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