Traditional rule-based systems, once sufficient for detecting simple patterns of fraud, have been overwhelmed by the scale, ...

Abstract: Fraud in supply chain operations poses significant risks to businesses, including financial losses, operational inefficiencies, and erosion of stakeholder trust. With the increasing ...

The review reports that blockchain-enhanced federated learning systems typically achieve slightly lower raw accuracy than ...

Overview: AI-powered fraud detection tools are rapidly being adopted by banks and fintechs to block scams and reduce losses.New platforms combine machine learni ...

The Federal Reserve is racing to contain a new kind of systemic risk, one that does not start with bad loans or exotic ...

Overview: AI in financial services uses machine learning and automation to analyze data in real time, improving speed, accuracy, and decision-making across bank ...

Fraud detection is no longer enough to protect today’s financial ecosystem. As digital transactions increase, banks require ...

The financial sector is anticipated to experience a notable surge in fraudulent activities, leading to projected losses exceeding $40 billion by 2027. This increase marks a significant uptick from ...

Today’s fast-paced online world is underlined by systems that allow it to move that fast. Whether it’s the latest advancements to transport systems, faster internet connections, or more real-time ...

Discover the 7 best fraud detection systems for enterprises in 2025. Learn about their features, pricing, and how they help combat digital and identity fraud in the ever-evolving threat landscape.

“Fraud detection today is about precision, not just protection. The ability to differentiate legitimate customers from suspicious activity in milliseconds is what separates high-performing businesses ...