SF-DynFinGC: Scale-Free Dynamic Graph Construction for Event-Driven Spatio-Temporal Learning in Financial Anomaly Detection

Existing machine learning methods for financial anomaly detection (FAD) typically overlook the complex spatio-temporal interactions inherent in financial data, significantly limiting detection accuracy and interpretability. While graph-based approaches have shown promise in modeling relational structures, existing methods mainly rely on static or overly simplistic dynamic graph constructions, struggling to capture these complex spatio-temporal interactions. To address these limitations, we propose SF-DynFinGC, a novel dynamic graph construction framework specifically tailored for event-driven spatio-temporal learning in FAD. SF-DynFinGC integrates scale-free structural regularization with a decay-governed global Top-k edge selection strategy, effectively reflecting real-world hierarchical and dispersed financial network characteristics. Through dynamically evolving graph structures triggered by financial events rather than fixed intervals, SF-DynFinGC accommodates irregular temporal and spatial data distributions. Comprehensive experiments on various benchmark datasets demonstrate that SF-DynFinGC consistently achieves state-of-the-art performance across graph neural network (GNN) and traditional classifiers, significantly outperforming existing static and dynamic graph construction approaches. Structural analyses further reveal that SF-DynFinGC generates interpretable and appropriately sparse graphs, highlighting critical transactional relationships indicative of complex anomaly patterns. This study provides a robust and interpretable dynamic graph construction framework that enhances both detection accuracy and analytical transparency, thereby extending the practical applicability of graphbased methods in financial risk management.