Preventing Payment Fraud with Generative AI Tools

Traditional rules-based systems for detecting payment fraud are increasingly inadequate due to the complex schemes devised by fraudsters. This leads to high false positives and limited adaptability. Predictive artificial intelligence (AI) improves this by reducing false positives and adapting to new patterns using machine learning. However, generative AI, which uses unsupervised or semi-supervised learning techniques, excels at detecting subtle and novel fraud patterns in unstructured data. This significantly improves fraud detection capabilities.

Modern payment fraud requires real-time learning and adaptation at scale. Generative AI offers the unprecedented benefit of continuous learning. She quickly refines and adapts her understanding of patterns to more accurately distinguish legitimate from fraudulent payments. Additionally, generative AI can produce synthetic data sets that mimic real-world financial data. This enables robust model training without compromising privacy or compliance.

Generative AI also improves the consumer experience by reducing false positives. Customers often feel frustrated when merchant checkouts incorrectly report legitimate transactions. By more accurately distinguishing between genuine and fraudulent behavior, generative AI ensures smoother transaction experiences and less customer frustration.

THE “Generative AI Tracker®“uncovers the innovative capabilities and emerging use cases of generative AI in transforming fraud prevention in the payments industry, while examining the privacy concerns, biases, and regulatory hurdles that must be overcome for wider adoption.

Leading the way in generative AI for payment fraud detection

The rise of generative AI hasn’t gone unnoticed by the financial industry. Two industry stalwarts—Visa and Mastercard—have already built and deployed their own in-house generative AI payment fraud detection tools. While the industry’s use of the technology is still in its infancy, these companies are providing insight into the different ways generative AI can be used to combat payment fraud—and early adopters are already seeing tangible benefits.

To learn more, visit the Tracker's Notable Companies section.

Generative AI could reduce the noise of fraud detection

The potential for generative AI to reduce the costly headache of payment fraud has attracted considerable attention in the financial sector. As this technology continues to mature and its adoption gains traction, it could become the cornerstone of modern payment fraud prevention strategies, promising improvements in accuracy, efficiency and cost reduction.

Much of the excitement surrounding this technology is due to its ability to overcome the limitations inherent in traditional fraud detection systems. Its capabilities could complement current methods by identifying and neutralizing payment fraud in real time, a prospect that could have implications for protecting the shopping experience and improving the bottom line of financial institutions and businesses. .

To learn more, visit the Tracker Innovation and Use Cases section.

The difficult path to defeating payment fraud

The breadth and impact of generative AI’s use cases help explain the financial industry’s excitement about its potential to combat fraud, with 83% of financial institutions already considering its use for these purposes. However, a sobering reality tempers this widespread enthusiasm: the same sophistication that makes the tool so powerful against fraud also acts as a barrier to its industry-wide adoption.

To learn more, visit the Issues and Challenges section of the Tracker.

About the tracker

THE “Generative AI Tracker®“discovers the innovative capabilities and emerging use cases of generative AI in transforming fraud prevention in the payments industry, while examining the privacy issues, biases, and regulatory hurdles that must be overcome for broader adoption.