3D Secure for addressing card-not-present fraud

The case for 3D Secure gets stronger as EMV swings into gear in the U.S.

Participants of the recent North America Fraud Forum in Chicago asked a lot of questions about how best to tackle Card-not-present (CNP) fraud. Based on responses, a clear answer is to battle CNP fraud using a 3D Secure (3DS) solution.

3DS cheerleaders are often met with naysayers who voice concerns about customer friction and the resulting loss in revenue after implementation, so my goal here is to give a high-level understanding of a customer-friendly, frictionless and streamlined 3D Secure solution.

Card-not-present fraud in the e-commerce space is the bane of consumers, banks, and merchants, and a primary focus is to cost effectively control CNP fraud from the transaction initiation point. Concerns are heightened since the industry has seen an increase in CNP fraud as EMV implementations proceed globally and the U.S. deadline has come and gone.

3DS is a viable and proven option to protect consumers, merchants and card issuers from CNP fraud, but how do you convince the naysayers?  What are the technical innovations you can turn to that stifle any argument of “barrier” or “cost” or “compliance”?

Keep consumers happy with streamlined, frictionless authentication as-a-service

With a powerful set of tools that includes sophisticated risk analytics, a self-learning neural network model and the ability to control fraud thresholds, card issuers can better authenticate genuine cardholder transactions. This means cardholders can proceed without interruptions or friction in the checkout process.

These technologies, when used together, identify a legitimate cardholder invisibly with a “zero-touch” system, bypassing the authentication interruptions typically experienced with legacy 3DS solutions.

Specifically, business risk policies can be applied with adaptable, dynamic rules to flag suspicious transactions that require stronger authentication. Alerts can be sent for further authentication steps or a transaction can be denied. Advanced neural network models can transparently assess transaction risk in real-time and pinpoint fraudulent activity occurring amidst legitimate ones. These behavioral models are powered by a sophisticated engine that captures and learns individual level cardholder behavior.

Fraud data can be immediately available to fraud analysts with 100 percent transparency. This allows the majority of transactions to be authenticated without requiring customer intervention while enabling rapid action against suspected fraudulent activity.

Cost and time savings through 3DS program enrollment

Operational support areas and IT resources can see improved operational savings by adapting techniques needed for new threats. Fraud analysts can also see a reduction in time spent writing rules to capture edge cases and increases time to perform other data analysis activities.

Banks have increased revenue, decreased abandonment rates to less than one percent and improved 3DS program enrollment by using 3D Secure payment solutions from CA Technologies that incorporate risk analytics and patent-pending neural network models.

This optimized, patent-pending neural network model is continuously analyzing between 50 and 200 variables and over 40,000 types of data while watching for real-time fraud trends occurring around the world. More importantly it is learning cardholder behavior making the model smarter about identifying legitimate transactions.

The CNP fraud trends we face today require highly sophisticated adaptive security solutions and require implementing the smartest methods to cost effectively manage risk.

With CA’s zero-touch 3D Secure authentication, customers gain precision in distinguishing fraudulent transactions from legitimate ones and a frictionless, easy checkout experience with the cost-efficiency of SaaS.


Carol Alexander (@carolalexander) is head of Product Marketing for Digital Payments Security at CA Technologies.

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