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 ...

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 ...

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 ...

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

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 ...

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 ...

This raises the question: is TransUnion poised for further growth, or has the market already accounted for its potential? TransUnion's recent upgrade arrives at a pivotal moment, with many analysts ...

“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 ...