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

Proactive monitoring tools, such as a third-party hotline platform and data analytics, coupled with employee engagement and a ...

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.

A new machine learning breakthrough outperforms traditional methods by reducing false positives and minimizing cases needing further inspection, crucial for sectors like Medicare and credit card fraud ...

Forensic analysis has become a cornerstone in the fight against financial fraud, combining accounting, auditing, and investigative skills to uncover and prevent fraudulent activities. As financial ...

When embedded within a case management framework, Agentic AI can detect subtle anomalies, highlight inconsistencies, and surface patterns traditional systems miss.

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

Database design and management are essential pillars of success in the financial sector, where efficient data handling and adherence to regulatory standards are critical. The global market for ...

With AI technology, payment fraud and fraud prevention have become more complex and sophisticated. Both financial data owners and businesses that accept illegal payments are directly affected by ...

Even though check use is declining in the U.S., incidents of check fraud and the value associated with check fraud is increasing. So much so that financial institutions have identified check fraud as ...