This study proposes a method for automatic audit report generation and compliance analysis based on the BERT model, aiming to solve the problems of low efficiency, high labor costs, and insufficient compliance detection accuracy in traditional audit work. By combining the powerful semantic understanding ability of the pre-trained language model, this study achieves high-quality generation of audit reports and uses classification tasks to accurately identify potential risk points in the text. The experiment uses multiple public datasets to evaluate the performance of BERT in text generation quality (BLEU Score) and compliance detection (Accuracy, F1-score). Compared with models such as GPT, T5, and LSTM, BERT performs best in all indicators. At the same time, through robustness testing, the performance of the model in the face of text noise or tampered data is analyzed. The results show that although noise has a certain impact on model performance, BERT's overall performance is still better than that of other comparison models. This study also explores the impact of different fine-tuning strategies on model performance and analyzes its adaptability and limitations in the audit field. The research findings demonstrate that the BERT-based methodology not only effectively enhances the quality of the automated generation of audit reports but also substantially improves the accuracy of compliance analysis. This advancement offers novel insights and serves as a guiding framework for the development of intelligent audit technology.

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 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.

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.

The inherent complexity and dynamic characteristics of diverse datasets present significant challenges for achieving high predictive accuracy in forecasting tasks. This study tackles these challenges by implementing a hybrid ensemble model aimed at enhancing predictive performance across imbalanced datasets. Using data from a competitive data source, the approach integrates LightGBM, XGBoost, and Logistic Regression models within a weighted ensemble framework to improve overall prediction accuracy. Data preprocessing techniques, including KNN imputation, Z-score normalization, and SMOTE, are employed to handle missing values, outliers, and class imbalances, ensuring a robust input for model training. The ensemble framework incorporates a Focal Loss function to specifically address class imbalances and refine prediction precision. Comparative analyses reveal that the proposed ensemble model consistently outperforms individual models in terms of accuracy, precision, recall, and AUC. This study offers a versatile and reliable solution for forecasting challenges, demonstrating enhanced robustness and broad applicability across domains.