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

Fraud detection is defined by a structural imbalance that has long challenged data-driven systems. Fraudulent transactions typically account for a fraction of a percent of total transaction volume, ...

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 ...

Digitalisation has rapidly changed the face of industry and business. Businesses have increasingly integrated modern technologies into their operations to improve real-time activity. However, ...

A surge in digital payment technologies has been paralleled by an equally rapid increase in credit card fraud. This research field explores multifaceted approaches that combine advanced analytics, ...

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 ...

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.

The surge in digital payments and mobile banking has transformed financial services but it has also expanded the fraud landscape. Traditional, rule-based fraud detection methods are increasingly ...

DataVisor, the world’s leading AI-powered fraud and risk platform, today announced that it has been selected as winner of the “AI-based Fraud Prevention Solution of the Year” award in the 8th annual ...