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 user risk classification model that combines dynamic graph neural networks with a self-supervised pretraining mechanism. The model is designed to address the dual challenges of dynamic user behavior and label scarcity in financial scenarios. First, a time-series graph is constructed to represent user transaction behaviors across different time slices. A dynamic graph convolution module is then applied to extract evolutionary features. Next, a node-level contrastive self-supervised task is designed. By constructing positive and negative node pairs, the model adaptively optimizes the representation space. This enhances the discriminative power of node embeddings during the unsupervised stage. Finally, the pretrained node representations are fed into a multi-layer perceptron for the downstream risk classification task. An empirical analysis is conducted on a publicly available credit card fraud detection dataset. The results show that the proposed model outperforms existing dynamic graph models in terms of accuracy, recall, and F1-score. It demonstrates a stronger ability in identifying high-risk user behavior, validating the effectiveness and robustness of the approach.

This paper proposes a stock price prediction model based on a combination method of recurrent neural networks (RNN) and attention mechanisms. The goal is to improve the forecasting accuracy of financial time series data. By applying the attention mechanism to the RNN architecture, the model can learn dynamic importance weights for different time steps. This allows better capture of dominant features in stock price volatility. The study utilizes real Apple Inc.'s stock prices between January 2024 and January 2025 to build models and validate the models. Normalizing and implementing train-test strategies were used to preprocess data in a bid to enhance stability within the models. Experiments utilize mean squared error (MSE), mean absolute error (MAE), and the coefficient of determination (R2) as evaluation metrics for the model. Comparative evaluation was performed against a set of mainstream popular models. Results show that the constructed RNN+Attention model outperforms traditional RNN, LSTM, MLP, and Transformer models both in prediction accuracy and fitting performance. In addition, visualizations demonstrate a strong correspondence between predicted values and actual values. The curve of training loss shows a clear downward trend, further supporting the effectiveness and convergence of the model. This method provides an efficient and realistic technical solution to stock price prediction using deep learning.