In today’s increasingly digital payments landscape, being human is important.
The contextual and behavioral cues that power payment anomaly detection models have become focal points in the ongoing fight against fraudulent activities, particularly as bad actors turn to tools like generative artificial intelligence to scale their attacks.
Every individual has a unique pattern of conducting transactions, characterized by a combination of habits, preferences and contextual nuances. Recognizing and deciphering these patterns is crucial for distinguishing between legitimate and fraudulent transactions.
Contextual and behavioral cues can encompass a range of factors, including device information, location data, transaction history and user preferences. These cues have taken center stage as the linchpin in identifying the authenticity of transactions.
Across today’s transactional landscape, where payments occur in milliseconds and across diverse platforms, relying solely on traditional security measures to cross-reference these cues is no longer sufficient.
Historically, fraud detection systems have relied on rule-based approaches that set predefined parameters for what is considered normal behavior. However, such systems often struggle to adapt to the dynamic nature of contemporary fraud attacks.
That’s where AI steps in, bringing a level of sophistication that goes beyond rigid rule sets.
When added to conventional behavior-monitored systems, AI and machine learning systems are tools that can help organizations increase payment authorization rates while minimizing fraudulent transactions.
See also: Demystifying AI’s Capabilities for Use in Payments
The integration of AI and ML into conventional behavior-monitored systems marks a leap forward in the fight against fraud. While traditional systems may excel at recognizing known patterns, they often struggle with detecting novel or sophisticated forms of fraud. AI augments these systems by continuously learning and adapting to emerging threats.
“As we start seeing more and more back-office systems for corporates move toward APIs, you’re going to have wires, ACH payments and instant payments all coming across APIs, and that’s going to give us the ability to start changing holistically all the tools we have in place to mitigate risk,” Irfan Ahmad, managing director and head of U.S. payments GTS at Bank of America, told PYMNTS in December.
AI-powered systems excel at analyzing multifaceted cues in real time, discerning patterns that may be imperceptible to traditional fraud detection methods. By understanding the intricacies of individual behavior, AI/ML systems create a comprehensive profile for each user, enabling firms to differentiate between legitimate and suspicious transactions and reduce payment friction for end-users.
“The folks that invest heavily in AI and use it to improve their authorization rates in the payment space are doing very well, as far as I’m concerned,” Aeropay Chief Revenue Officer Andrew Gleiser told PYMNTS in May. “Improving authorization rates by 10%, for example, ends up hitting the top-line revenue of both the processor and their customer.”
Read also: Why Whack-a-Mole Risk Prevention Won’t Work in Today’s Data Economy
ML algorithms analyze vast amounts of data, learning from every transaction to enhance their ability to identify fraudulent activity. This dynamic approach allows AI systems to evolve alongside the ever-changing tactics employed by fraudsters, providing a proactive defense against new and evolving threats.
“Things like fraud detection, forecasting, anomaly detection and recommendations have existed for a very long time,” Billtrust Senior Vice President of Data Analytics and AI Ahsan Shah told PYMNTS in December.
“What is fundamentally changing is that AI is redefining itself this year with generative AI, he added.
In the pursuit of preventing fraud, the goal is not only to thwart unauthorized transactions but also to streamline the authorization process for legitimate ones.
“You also need a high-quality risk score that enables you to understand where to apply the right friction,” Nick Fleetwood, head of data services at Form3, told PYMNTS this month.
By accurately identifying authentic transactions based on contextual and behavioral cues, AI/ML systems contribute to a more seamless and efficient payment experience for users by minimizing the likelihood of false positives.
“In the payments security space, just as in the consumer space, you’re seeing a massive investment in AI,” Jeff Hallenbeck, head of financial partnerships at Forter, told PYMNTS in October. “It’s a buzzword, but it has to be, as bad actors evolve in the ways they are attacking businesses.”
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