FFD-DHG: Financial Fraud Detection Based on Dynamic Heterogeneous Graph Representation Learning

With the expansion of the capital market, financial fraud incidents have become increasingly frequent.Existing financial fraud detection methods have made some progress, but most approaches overlook at least one critical issue: First, they rely solely on single data sources or static data, failing to effectively capture complex dynamic relationships between companies and collaborative behaviors across time.Second, they struggle to address "disguised behavior" in financial fraud, where fraudulent companies establish relationships with legitimate entities to conceal their actions, leading to data inconsistencies.Third, they lack modeling of temporal dependencies, failing to adequately capture changes in corporate behavior across different time periods.To address these issues, we propose FFD-DHG, a financial fraud detection method based on dynamic heterogeneous graph representation learning.The method uses a fraud-aware graph convolution encoder to capture static topological structure information at each discrete time snapshot and aggregates neighborhood difference information for target nodes to mitigate the inconsistency caused by fraud camouflage while enhancing the distinguishability between normal and fraudulent nodes.Subsequently, we propose a feature fusion module using reinforcement learning, aiming to adaptively adjust the filtering threshold during different node feature aggregation processes to reduce noise interference.To capture temporal dependencies, we introduce a sliding window mechanism and temporal encoding function to model cross-temporal context similarity, thereby more accurately identifying concealed fraudulent behaviors.To validate the practical effectiveness of the proposed financial risk identification method, experiments are conducted on a real-world dataset.Furthermore, the model interpretability analysis of FFD-DHG reveals key features affecting financial fraud detection, providing a decision-making basis for regulatory authorities.