In this paper, we examine to what extent the employment of machine learning technique contributes to better detection and prediction of corporate (i.e., firm-level) accounting fraud. The obtained ...

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

Detecting and preventing accounting fraud is a concern for many policymakers around the world. This column presents a framework that incorporates machine learning techniques to detect and forecast ...

Fraud is widespread in the United States and increasingly driven by technology. For example, 93% of credit card fraud now involves remote account access, not physical theft. In 2023, fraud losses ...

Launching a digital wallet today involves far more than enabling payments. As the digital wallet trends 2026 show high adoption of digital wallets, so do the challenges like increasingly sophisticated ...

For years, fraud prevention has followed a familiar script. A transaction is initiated. A model evaluates it. Fraud still ...

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 ...

Machine learning often feels difficult at the beginning, especially when everything stays theoretical. That changes once you ...