Analysis of digital intelligent financial audit system based on improved BiLSTM neural network - Zhu, Xincai (Nonlinear engineering, 2025)
Traditional auditing methods have difficulties in detecting various financial issues hidden in massive amounts of data. With the continuous advancement of deep learning and digital technology, new audit methods have been provided for computer auditing. Therefore, in order to achieve intelligent analysis and processing of audit reports, this study innovatively applies the convolution operation of convolutional neural networks to the forward and backward layers of bidirectional long short-term memory networks, obtaining more accurate feature recognition and prediction results. Meanwhile, by introducing radial basis functions for nonlinear mapping of the data space, the model’s ability to fit complex data is enhanced, thereby improving the analytical capability of the digital intelligent financial audit system. The experimental results show that the comprehensive average accuracy of the improved algorithm reaches 92.31%, and the 1-score of the reconciliation function reaches 80.55, which are significantly higher than the other four algorithms. This indicates that the digital intelligent financial audit model proposed in this study can accurately analyze financial audit data, proving that it can comprehensively process various types of financial data and effectively improve the efficiency of modern enterprise audit data analysis.