This paper proposes an anomaly detection algorithm based on large language models guided by knowledge graphs. The method combines the deep semantic understanding capability of large language models with the structural modeling advantages of knowledge graphs, forming a semantic-structural co-driven fraud detection framework. Specifically, the model first encodes transaction texts using a pretrained language model to generate semantic representations. Meanwhile, a financial knowledge graph is constructed based on entities such as transaction accounts, devices, and IP addresses, from which structural embeddings are obtained via graph neural networks. Next, a gated fusion mechanism is introduced to adaptively integrate the semantic and structural vectors. The fused representation is then fed into a classifier to produce fraud prediction results. In multiple comparative experiments, the proposed model outperforms single-modality approaches across metrics including accuracy, precision, recall, and F1-score, validating its effectiveness in complex financial scenarios. In addition, this study conducts multi-task adaptation experiments and anomaly ratio variation tests to evaluate the model's stability and robustness. The results show that the proposed method maintains strong performance across different environments, demonstrating its reliability and practical value.

This paper proposes a deep learning model for systemic risk identification and prediction in financial markets, integrating multi-scale convolutional structures, attention mechanisms, and residual connections. The model is designed to capture dynamic features at different temporal resolutions, enhance focus on key risk factors, and maintain training stability in complex financial data environments. Using the US Systemic Risk Dataset released by the Federal Reserve, the model transforms the task into a binary classification problem based on crisis-related indicators. All time series data are standardized, interpolated, and structured through sliding windows to preserve temporal continuity. Experimental comparisons show that the proposed model outperforms traditional methods such as MLP, CNN, Transformer, and BERT across accuracy, precision, and recall. Further analysis reveals the effectiveness of multi-scale and attention mechanisms in modeling complex dependencies and detecting early risk signals. Sensitivity experiments on convolutional kernel size demonstrate that integrating cross-feature information significantly improves performance, with 3x3 and 5x3 kernels showing optimal results. The training process exhibits fast convergence and low loss stability, confirming the model's robustness and efficiency. These findings validate the technical advantages of the proposed architecture in addressing the challenges of systemic risk modeling in high-dimensional and heterogeneous financial time series.

This study proposes a multi-asset portfolio risk prediction model that integrates Long Short-Term Memory (LSTM) networks with Copula functions. The model is designed to capture both the temporal dynamics of financial asset returns and the nonlinear dependencies among assets. LSTM is used to model the marginal distributions of individual asset return series. Copula functions are employed to describe the joint distribution structure across multiple assets. This allows for accurate prediction of key portfolio risk metrics such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). In the experimental design, several baseline models are constructed for performance comparison. Further analyses are conducted to assess the model's risk prediction ability under varying numbers of assets and to evaluate risk coverage across different confidence intervals. The experimental results show that the proposed LSTM-Copula model outperforms mainstream methods across multiple evaluation metrics. It demonstrates higher robustness and predictive accuracy, particularly in high-dimensional and sparse data settings. This approach offers a novel path for financial risk management by combining statistical modeling with deep learning. It provides strong empirical results and holds substantial practical value for applications in complex financial environments.

This study proposes a graph neural network (GNN)- based method for fraud detection in financial transaction networks. By deeply modeling the transaction graph structure and the relationships between nodes, the method enhances the ability to identify complex fraudulent behaviors. First, financial transaction data is transformed into a graph structure, where transaction accounts are represented as nodes and the flow of funds as edges. Node features such as transaction amount and timestamp are used for node representation learning. Next, graph neural network architectures, such as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT), are employed to uncover the latent associations and interactions between nodes. This helps identify anomalous fraudulent transactions. To improve the model's generalization and robustness, the study also introduces contrastive learning strategies and imbalance handling techniques. A weighted loss function is used to optimize the model's performance on minority fraud samples. Experimental results show that the proposed method outperforms traditional machine learning models and other deep learning approaches across various evaluation metrics on publicly available datasets. Notably, it achieves a better balance between precision and recall. This method effectively combines graph structural information with deep learning techniques, providing a novel solution for intelligent financial risk control.

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.