Graph-based AI approaches for supply chain risk and fraud detection

Supply Chain Risk Management (SCRM) seeks to identify, assess, and mitigate risks such as fraud, operational delays, and financial irregularities to enhance supply chain resilience. This study introduces an Adversarial Autoencoder-based Heterogeneous Graph Neural Network (AAHGNN) that addresses the complexities of supply-chain fraud detection in networks with intricate supplier–customer relationships. The proposed approach integrates graph convolution, neighborhood aggregation, adversarial representation learning, and graph attention. Financial metrics and supplier–customer interactions were utilized to construct a supply-chain knowledge graph. The framework was evaluated against baseline models, including SVM, CNN, GNN, RNN-LSTM, and GCNN. AAHGNN achieved an accuracy of 87.62%, a precision of 90.34%, a recall of 89.57%, and an [Formula: see text]-score of 90.17%, while the baseline GCNN achieved 91.39% accuracy and 96.87% AUC. Furthermore, the attention mechanism in AAHGNN highlights critical supplier–customer relationships associated with fraud, underscoring its effectiveness for explainable supply chain risk management.