Corporate Fraud Detection in Rich‑yet‑Noisy Financial Graph
This paper tackles corporate fraud detection using real-world Chinese stock market data. It highlights challenges like information overload and hidden fraud. The proposed KeGCNR model enhances detection with knowledge graph embeddings and robust training. Experiments show superior performance. Future research should address class imbalance and IND noise. Public datasets are provided.