[Editor's note: December is Machine Learning Month at Card Not Present (sponsored by Feedzai). Fraud in the digital commerce world continues to increase not only in volume, but also in sophistication. Higher order threats require a different response, and antifraud systems based on machine learning are becoming an important part of an online merchant's arsenal. Check back here throughout the month for updated content detailing the way machine learning technology is changing the face of fraud prevention.]
In the wake of increasing fraud, growing competition and declining profits, merchant acquirers have no shortage of challenges ahead of them. New technology is creating more roadblocks—and attack vectors—than ever before.
Fortunately, acquirers aren't alone in the fight. Machine learning is rapidly evolving, and is on its way to becoming an essential part of the merchant acquirer's fraud management strategy. Let's review a few of the top challenges facing acquirers and how machine learning can help.
1. Increased competition
It's no secret that industry disruptors are pressuring traditional acquirers with new services that are more convenient and eliminate inefficiency. Stripe and Adyen are just two examples; merchants flock to innovators that can offer frictionless services and better experiences for their customers, and traditional merchant acquirers are struggling to keep up.
2. Lower profit margins
Remember when customers were excited to shop online and merchant acquirer profit margins were multiplying by the year? No longer. Basic services have become commoditized to the point where acquirer profit margins are razor thin, with CAGR projections showing meager growth of 2 to 3 percent.
3. Evolving fraud
The changing fraud landscape is the biggest threat to acquirers. Shifts from card-present (CP) fraud to card-not-present (CNP) strategies place the burden of risk on merchants, and the never-ending consumer preference for new channels (such as mobile) makes comprehensive security a daunting task.
Few acquirers (or their merchants, for that matter) can bear the cost of fraud. Every breach produces a loss of revenue and possible fines—but even worse, it leads to a negative customer experience that can put an acquirer's reputation in jeopardy. And, with the October 2016 Nilson Report noting that worldwide card fraud may reach $32 billion as early as 2020, merchant acquirers are running out of time to come up with solutions.
How Can Acquirers Fight Back?
The twin forces of competition and fraud have put acquirers in a challenging place. How can companies fight the fast-moving world of fraud without sacrificing customer experiences with needlessly complex security?
Become All-In-One Solutions
The Electronic Transaction Association reports that over 70 percent of merchants cite poor service as the primary reason for leaving a payments processor. With competition at an all-time high, this type of attrition is untenable.
Acquirers need to examine how they can expand their offerings to better meet the needs of their clients, from transaction processing to risk management. Many next-gen payment processors already deploy these types of value-added services, and it's not hard to see why. Merchants go with the processors who make their lives easiest.
And naturally, these types of seamless experience are only found through acquirers that provide adequate security on top of transaction efficiency. As such, fraud management solutions are becoming an essential service offering for merchant customers.
This brings us back to machine learning.
Leverage Machine Learning for Fraud Detection
Machine learning is the merchant acquirer's secret weapon against fraud. By leveraging algorithmic assessments that extend fraud detection beyond traditional rules-based systems, acquirers expand their risk management capabilities and create solutions that their merchants can count on.
Partners like Feedzai offer this type of multi-tenant fraud management architecture in simple packages that acquirers can use to build more intelligent risk models.
The secret lies in how the multi-tenant approach is combined with machine learning. Multi-tenant architecture means that Feedzai can tap into the network of every client connected to the platform and extract insights from each acquirer's customer activity.
Rather than assessing each acquirer's data individually, the machine learning platform pulls data from all of them at once and uses algorithmic assessments to learn about which actions, triggers and escalations indicate suspicious activity. Backed by this network of resources, acquirers gain several advantages:
- Immediate fraud detection to reduce chargebacks, false positives, and declines
- Faster and seamless merchant onboarding processes
- Implement transactional scoring
- Reduced friction across all aspects of the customer experience
Machine learning is revolutionizing the financial industry across all fronts, from data analysis to transparency to fraud detection. But for some merchant acquirers, the improvements are happening too late. Growing competition and advanced fraud are seismic shifts forcing providers to re-evaluate their priorities and consider new ways to meet consumer demand—but without machine learning, these advancements may be impossible to implement.
We did a full analysis of these challenges that you can download for free here.