A semantic and behavioral AI framework for detecting invoice fraud in automated accounts payable

As companies adopt automation to streamline their accounts payable (AP) functions, they are also facing a new generation of invoice fraud - more subtle, more complex, and often undetectable by traditional control systems. This paper proposes a new fraud detection approach that merges semantic analysis with behavioral pattern recognition to expose tactics such as slightly altered duplicate invoices and collusive activity between internal staff and vendors. It shows how to blend behavioral anomaly detection with transformer-based NLP to identify unusual transactional behavior accurately. It does so via synthetic accounts payable and artificial ERP files. Blending these techniques gives business practitioners easily interpretable, straightforward results that can be used to make choices and facilitate action. The framework is designed to fit within existing ERP environments with minimal disruption, delivering real-time risk scoring, strong audit traceability, and the flexibility to adjust to organizational changes over time. This paper draws from hands -on examples and performance reviews to show how the proposed framework tackles a major oversight in today’s AP automation tools - specifically, their limited ability to detect nuanced or hidden forms of fraud. Although our evaluation relied on simulated data, the findings are encouraging and point to real practical value. With continued refinement and validation in real-world environments, this approach has the potential to give finance teams a stronger, more adaptive tool for identifying suspicious activity before it escalates.