Knowledge Graph-Driven Generative Framework for Interpretable Financial Fraud Detection

This paper proposes a knowledge-graph-guided generative detection model to address the challenges of complex feature associations, hidden behavioral patterns, and sparse semantic information in financial fraud detection. The study maps multi-source heterogeneous data, such as accounts, transactions, devices, and geographic information, into a knowledge graph structure to model semantic dependencies and behavioral relationships among financial entities at both node and relation levels. Based on this, the model introduces a relation-aware encoder to obtain high-dimensional structural embeddings and employs a generative reasoning mechanism to learn the distribution of latent fraud patterns, enabling semantic identification of complex risk behaviors without relying on fixed rules. During generation, the method integrates knowledge constraints and structural priors, allowing the model to reconstruct potential fraud chains from a global semantic perspective and significantly enhance interpretability and generalization. Validation on real transaction datasets shows that the model outperforms comparison methods in AUC, ACC, Precision, and Recall, demonstrating the effectiveness of the knowledge-enhanced generative framework in capturing cross-entity dependencies and dynamic risk relationships. Overall, the proposed knowledge-graph-guided generative detection model achieves a unified framework of structural modeling, semantic reasoning, and anomaly generation, providing a new systematic solution for the intelligent detection of complex financial fraud behaviors. CCS CONCEPTS: Computing methodologies~Machine learning~Machine learning approaches.