Using behavior analytics, machine learning and global transaction data, CA Technologies latest tool can help prevent fraud for payment cards.

Called CA Risk Analytics Network, the new tool is a cloud-based service that incorporates advanced neural networks, backed by real-time machine learning, to protect 3-D Secure card-not-present (CNP) transactions.

According to CA, 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.

The Javelin’s 2017 Identity Fraud Report found 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.

The report read: “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 per cent increase in those who had their cards misused in a CNP transaction in 2016, compared to 2015 levels.”

Terrence Clark, general manager for CA Technologies Payment Security solutions, said detecting anomalies quickly and ensuring frictionless authentication are the first steps in preventing card-not-present fraud without impacting legitimate cardholder transactions. Data scientists have applied advanced analytics and new, real-time, machine learning algorithms to the global pool of 3-D Secure, e-commerce transaction data and device insights maintained by the CA Payment Security Suite.

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. 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.



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