CA’s new Risk Analytics helps card issuers combat fraud

  • Adds new behavioural neural network authentication models
  • More accurate fraud detection for improved online shopping experience    
CA’s new Risk Analytics helps card issuers combat fraud

CA Technologies said its new release of CA Risk Analytics includes intelligent, self-learning authentication technologies that help reduce friction for consumers during online checkout.
They also allow card issuers to reduce incidents of fraud, increase revenue, and gain new levels of flexibility and control in their fraud detection systems, the company said in a statement.

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This latest version of CA Risk Analytics incorporates patent-pending behavioural neural network authentication models for assessing risk of online, card-not-present (CNP) transactions.
The neural network models are powered by machine-learning techniques that capture data about individual user actions and enable CA Risk Analytics to better understand and distinguish legitimate from fraudulent behaviour.
Optimised for 3D Secure protocol, CA Risk Analytics prevents CNP fraud in 3D Secure transactions by transparently assessing the risk of a transaction in real-time, CA Technologies claimed.
CA’s new Risk Analytics helps card issuers combat fraud“History shows that the continued global rollout of the EMV (Europay/ MasterCard/ Visa) standard and the increasing distribution of chip and PIN cards will result in an increase of CNP fraud attempts,” said Vic Mankotia (pic), vice president of solution strategy at CA Technologies in Asia Pacific & Japan
“This demands a more advanced CNP fraud detection strategy that goes beyond just comparing the current transaction to established fraud indicators.
“CA Risk Analytics and its behavioural neural network models will result in ‘zero touch’ authentication that will instill a level of confidence and streamline the online checkout process,” he added.
CA said its Risk Analytics considers both fraud patterns and legitimate transaction behaviour and tracks the pivotal players in a transaction – card or device, for example. It estimates the risk of fraud using advanced machine learning techniques to understand normal behaviour for these pivotal players as well as the fraud risk related to deviation from past behaviours.
This results in a more accurate assessment of which transaction to authenticate and helps stop fraud in CNP transactions, the company said.
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