Mohan Jiang

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.

This paper delves into the application of adversarial domain adaptation (ADA) for enhancing credit risk assessment in financial institutions. It addresses two critical challenges: the cold start problem, where historical lending data is scarce, and the data imbalance issue, where high-risk transactions are underrepresented. The paper introduces an improved ADA framework, the Wasserstein Distance Weighted Adversarial Domain Adaptation Network (WD-WADA), which leverages the Wasserstein distance to align source and target domains effectively. The proposed method includes an innovative weighted strategy to tackle data imbalance, adjusting for both the class distribution and the difficulty level of predictions. The paper demonstrates that WD-WADA not only mitigates the cold start problem but also provides a more accurate measure of domain differences, leading to improved cross-domain credit risk assessment. Extensive experiments on real-world credit datasets validate the model's effectiveness, showcasing superior performance in cross-domain learning, classification accuracy, and model stability compared to traditional methods.