Research on Financial Credit Fraud Detection Methods Based on Temporal Behavioral Features and Transaction Network Topology

Abstract

The exponential growth of digital financial transactions has intensified the need for sophisticated fraud detection mechanisms. This research presents a novel approach integrating temporal behavioral pattern analysis with transaction network topology for enhanced credit fraud detection. Our methodology combines multi-modal temporal feature engineering with graph neural network architectures to capture both sequential behavioral patterns and spatial transaction relationships. The proposed framework employs adaptive attention mechanisms for temporal sequence modeling and spectral clustering for network anomaly detection. Experimental validation on real-world datasets demonstrates superior performance compared to traditional methods, achieving 94.7% precision and 92.3% recall. The integration of temporal and spatial features through our innovative fusion strategy addresses the limitations of existing single-modal approaches. The system demonstrates robust performance under varying fraud scenarios while maintaining computational efficiency suitable for real-time deployment. This research contributes a comprehensive framework that advances the state-of-the-art in financial fraud detection through the synergistic combination of temporal analytics and network topology analysis.