Graph Neural Network and Temporal Sequence Integration for AI-Powered Financial Compliance Detection

Paper proposes a compliance anomaly detection algorithm for financial transaction data, addressing the challenges of behavioral complexity, structural dependence, and strong temporal characteristics. The method constructs a dynamic trajectory graph with multi-type edges based on account interactions, providing a unified representation of structural relationships and temporal order in financial transactions. A graph neural network is employed to embed node relationships and extract high-order behavioral structure features. A gated recurrent neural network is then used to model the temporal dynamics of trajectory sequences and capture the evolution patterns of transaction behaviors. In the classification stage, a joint optimization strategy combines contrastive learning with an anomaly scoring function to enhance intra-class cohesion and inter-class separation, improving the model's ability to identify abnormal behaviors. To evaluate the effectiveness of the proposed method, experiments are conducted across various sensitivity dimensions, including time window length, trajectory length, node dropout rate, anomaly ratio, and class imbalance. The results show that the method achieves superior detection performance and strong adaptability under diverse perturbation conditions, demonstrating the potential of structure-temporal joint modeling for financial compliance anomaly detection tasks. CCS CONCEPTS: Computing methodologies~Machine learning~Machine learning approaches.