The machine learning technique FraudGCN analyzes patterns in financial data and corporate relationships to identify and predict fraudulent activities. It uses graph theory and machine learning to ...

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

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 ...

Researchers have developed a federated deep learning framework that detects financial fraud across banks with high accuracy while keeping all raw transaction data local.

Some of the challenges of financial transaction anomaly detection include the fact that fraud is rare, adaptive and costly to operate. In this study, we propose the ...

Dublin, July 31, 2025 (GLOBE NEWSWIRE) -- The "Strategic Intelligence: Deep Dive into Quantum Computing in Financial Services" report has been added to ResearchAndMarkets.com's offering. This report ...

In an increasingly digital financial landscape, fraud is evolving in both scale and complexity. Financial institutions from global banks to nimble fin-techs face constant threats ranging from payment ...

Learn how AI fraud detection uses payment and login signals to flag suspicious activity, reduce false positives and support ...

Researchers report a unified Android fraud detection framework combining kernel-level traffic decryption, a behavioral graph, ...

MOUNTAIN VIEW, Calif.--(BUSINESS WIRE)--Today, DataVisor, the world’s leading AI-powered fraud and risk platform, launched a groundbreaking solution that computes hotspot, distinct count, and ...

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