Top 3 card-not-present fraud prevention trends

June 25, 2019

There is a lot to get excited about when it comes to eCommerce. eMarketer projects global retail eCommerce sales are expected to exceed $3.4 trillion in 2019, rising to a remarkable $6.4 trillion by 2023. 

Yet eCommerce also faces challenges, first among them is the fight against card-not-present (CNP) payment fraud. Card-not-present fraud is on the rise, expecting to reach $24 billion in the US by 2020. The costs of fraud to eCommerce merchants go beyond direct financial losses to include the costs of prevention efforts and damaged business reputations. The “true costs of fraud” were estimated by LexisNexis  to represent 2.38% of eCommerce sales in 2018, a 6% increase over 2017.

You don’t need to become an expert in card-not-present fraud to protect your business—that’s where trusted payment security partners step up. But you will want to become a more informed consumer of fraud protection services. These three trends offer a glimpse into the future fight against eCommerce fraud. Contrary to many nay-sayers, that future is as bright as ever.

Machine learning, AI and the future of card-not-present fraud

Machine learning and artificial intelligence (AI) are essential tools in the battle against fraud. Most payment processors, financial institutions and card networks all employ at least some elements of these technologies in current static fraud detection and prevention efforts.

Machine learning plays a key role. This is where the promise of “big data” is paying real-life dividends. As the volume of transaction metadata rises, sophisticated data analytics reveal patterns that help model both “good” and “bad” behavior. Those patterns fuel machine learning algorithms that are—with human guidance—continually learning and adjusting at machine scale.

Machine learning and AI help reduce costs by automating manual processes, but that’s only the beginning. Machine learning examines payment transactions at a scale that would be impractical, if not impossible, for humans. That combination of processing power and real-time learning is especially important when fighting fast-moving and adaptable card-not-present fraud.

Machine learning and AI are already helping to reduce security costs related to operations and chargeback management, increase acceptance rates and thwart fraudsters in their tracks. These efforts are only beginning to bear fruit and pave the way for a more secure eCommerce future.

The future of secure authentication

Biometric authentication methods are now present in a variety of applications, including fingerprint, palm print, eye-scan, and voice recognition technologies that drive advanced security tools such as access to restricted areas and airline security screening.

Multifactor authentication is increasingly common and is now employed in a wide variety of online consumer services such as leading social media and email applications. Multifactor authentication requires confirming at least two among three options:

  • Knowledge (something you know, like an answer to a unique question)
  • Possession (something you have, like your phone or a payment card)
  • Biometric (something you are, like your fingerprint)

The future of card-not-present fraud detection and prevention will merge these concepts by introducing multifactor biometrics. Where passwords have proven highly penetrable and even two-factor authentication can be spoofed, multifactor biometrics will allow for highly-secure authentication that is nearly impossible to recreate by anyone other than the verified user.

Secure authentication is being pushed by consumers, businesses and now by regulatory initiative. Strong Customer Authentication (SCA) will become reality in the EU as of September 2019 as part of the PSD2 security mandates. As fraud and privacy continue to be top-of-mind among everyone in eCommerce, look for more security-conscious regulatory changes on the horizon.

Moving from static to dynamic fraud detection and prevention

In “Fraud Detection 2.0: Dynamic Tools for Fighting eCommerce Fraud," the Mercator Advisory Group outlined a progressive vision that offers tangible hope for a safer future of online commerce that includes a combination of:

  • Machine learning and AI
  • Behavioral biometrics
  • Persistent identity
  • Dynamic risk scoring
  • Mobile device ID
  • Persona linking

Mercator identifies proactive tools for the new age of fighting fraud to include "continuous learning and real-time updating, fewer manual reviews, reduced incidence of false positives, customized—deters fraudster spoofing and mimicking, and more sales and long-term customers.”

It may sound space-aged, but “multimodal behavioral biometrics” can create a persistent identity authentication that doesn’t require any active intervention on the part of the user. Now that’s frictionless commerce.

How to find the right partner to fight card-not-present fraud

Fraudsters are intelligent, relentless, and innovative. They understand the importance of continuous improvement and are constantly adjusting their tactics to stay one step ahead of the game. 

Financial institutions, card brand networks, security experts and payment processors are determined to make the future of eCommerce safe for consumers and businesses alike. These payment industry leaders are deploying state-of-the-art technology, rigorous processes and the best teams to protect the hard-earned reputation and bottom line of businesses.

eCommerce merchants have plenty of options when it comes to selecting a payments partner. So be sure to ask how they plan to fight fraud, now and in the future. Are they reliant on static methods and fighting the last battle? Or are they employing the latest technology and working with those that push the envelope of security? Look for partners who can point to demonstrated successes protecting eCommerce merchants just like you.

Connect with one of our payments experts today to lean how we protect eCommerce businesses with secure transactions that minimize fraud and reduce risk.