Enterprise Financial Fraud Detection and Audit Optimization Based on Deep Learning

Aiming at the problem of enterprise financial fraud detection, this study aims to improve the accuracy and efficiency of detection through deep learning (DL) technology, so as to optimize the audit process and provide intelligent financial management guidance for enterprises. To achieve this goal, an advanced financial fraud detection model based on DL is designed and verified. This research is not only devoted to automatic extraction and in-depth analysis of complex features in enterprise financial data through self-defined DL model architecture, but also enables the model to accurately learn the key patterns of identifying fraud through large-scale and high-dimensional financial data training. Furthermore, the DL model is integrated into the traditional audit process, which realizes the automation and intelligent upgrade of the process. The results show that the proposed DL model has achieved excellent performance improvement in financial fraud detection, and all the evaluation indexes are over 90%, which is obviously superior to the traditional methods. This study not only provides a new technical means to ensure the financial security of enterprises, but also enhances the value of audit work.