A new academic study argues that fraud detection systems must evolve beyond accuracy-focused prediction tools into ...

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

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

Fraudulent activities within the financial services sector have escalated into a significant concern, with projections indicating that online payment fraud could result in losses exceeding $206 ...

Fraud detection is no longer enough to protect today’s financial ecosystem. As digital transactions increase, banks require systems that can assess risk with precision.

“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 ...

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 ...