Ruizhe Zhou

This study addresses the challenges of financial risk early warning by proposing a modeling approach based on spatiotemporal Transformers. The research first examines the multidimensional characteristics of financial risk, emphasizing its temporal dynamics and cross-regional interactions. It notes that many existing methods struggle to jointly capture temporal dependencies and inter-regional risk transmission patterns. To overcome these limitations, a unified spatiotemporal modeling framework is developed. The framework integrates temporal encoding, spatial adjacency information, and multi-head attention mechanisms to model long-range dependencies and regional spillover effects. In the model architecture, an embedding layer is employed to learn representations from multi-source financial indicators. A self-attention mechanism facilitates global feature interaction, while a graph convolution component further enhances the modeling of spatial relationships across markets. The final risk representation is generated through a feed-forward network with normalization layers, providing a structured basis for financial risk assessment and early warning analysis. Experimental evaluations include comparative studies and sensitivity analyses under varying missing data ratios, time window settings, and environmental conditions. The results indicate that the proposed method consistently outperforms several baseline models in terms of accuracy, precision, recall, and F1-score. Overall, the approach demonstrates strong robustness and practical applicability in complex financial settings, offering an effective tool for financial risk monitoring and decision support.

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.