BERT-Based Automatic Audit Report Generation and Compliance Analysis

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.