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

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

“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 ...

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

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 ...

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

Srinubabu Kilaru said Bringing version control and CI/CD into data pipelines changed how quickly we could respond to policy ...

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.

Ravelin, a machine learning fraud detection company based in London, has raised approximately $3.7 million (£3M) in funding to support its growing global client base. The finance round was led by ...

As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models ...