Graph Neural Networks for Detecting Anomalous Transaction Patterns in Enterprise Accounting Systems

The increasing complexity of enterprise accounting systems has created significant challenges in detecting fraudulent and anomalous transaction patterns. Traditional rule-based detection methods often fail to identify sophisticated fraud schemes that exploit network structures and temporal dependencies within transaction data. This paper proposes a graph neural network-based framework for detecting anomalous transaction patterns in enterprise accounting systems by modeling financial transactions as heterogeneous graphs. We demonstrate that graph-based representations can capture complex relationships between accounts, merchants, and transactions that are invisible to conventional point-based anomaly detection methods. Our approach leverages multi-level graph analysis to detect anomalies at transaction, account, and network levels, enabling comprehensive fraud detection across different organizational scales. The methodology incorporates temporal dynamics, heterogeneous node types, and structural features to identify suspicious patterns indicative of fraudulent activities. Experimental results demonstrate the superiority of graph neural networks over traditional machine learning approaches in detecting complex fraud schemes including collusion networks, money laundering chains, and coordinated attack patterns. The proposed framework achieves significant improvements in detection accuracy while maintaining interpretability through graph visualization and attention mechanisms that highlight suspicious substructures.