Shiqing Long

This paper proposes a knowledge-graph-guided generative detection model to address the challenges of complex feature associations, hidden behavioral patterns, and sparse semantic information in financial fraud detection. The study maps multi-source heterogeneous data, such as accounts, transactions, devices, and geographic information, into a knowledge graph structure to model semantic dependencies and behavioral relationships among financial entities at both node and relation levels. Based on this, the model introduces a relation-aware encoder to obtain high-dimensional structural embeddings and employs a generative reasoning mechanism to learn the distribution of latent fraud patterns, enabling semantic identification of complex risk behaviors without relying on fixed rules. During generation, the method integrates knowledge constraints and structural priors, allowing the model to reconstruct potential fraud chains from a global semantic perspective and significantly enhance interpretability and generalization. Validation on real transaction datasets shows that the model outperforms comparison methods in AUC, ACC, Precision, and Recall, demonstrating the effectiveness of the knowledge-enhanced generative framework in capturing cross-entity dependencies and dynamic risk relationships. Overall, the proposed knowledge-graph-guided generative detection model achieves a unified framework of structural modeling, semantic reasoning, and anomaly generation, providing a new systematic solution for the intelligent detection of complex financial fraud behaviors. CCS CONCEPTS: Computing methodologies~Machine learning~Machine learning approaches.

This paper addresses key challenges in credit fraud detection, including small sample sizes, imbalanced data distribution, and rapidly changing fraud patterns. A meta-learning-based detection method is proposed to enhance the model's adaptability and detection efficiency in complex scenarios. The method adopts a two-level architecture consisting of a meta-learner and a task-learner. By extracting shared knowledge across multiple fraud detection tasks, the model can rapidly update its parameters. This allows it to maintain high detection performance when facing new types of fraud. In the method design, a task-level gradient update mechanism and a weighted loss strategy are introduced. These components help address the challenges caused by rare fraud samples and class imbalance. The proposed model is systematically evaluated through multiple experiments. These include different task quantities, varying degrees of data imbalance, and injected abnormal fraud patterns. The results confirm the model's effectiveness and robustness under diverse conditions. Experimental findings show that the proposed method outperforms traditional models in accuracy, precision, and recall. It also demonstrates better stability and generalization when facing dynamically changing fraud scenarios. This study highlights the application potential of meta-learning in credit fraud detection. It provides technical support for building more intelligent financial risk control models.

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.