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

Abstract

Existing machine learning methods for financial anomaly detection (FAD) typically overlook the complex spatiotemporal 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 spatiotemporal interactions. To address these limitations, we propose SF-DynFinGC, a novel dynamic graph construction framework specifically tailored for event-driven spatiotemporal 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 graph-based methods in financial risk management.