Audit risk prediction model based on deep learning: the impact of enterprise digital transformation on audit quality

Based on empirical evidence from Chinese A-share listed companies, this paper explores the application of deep learning technology in audit risk prediction and its impact on audit quality. The study shows that the degree of deep learning application in enterprises is significantly positively correlated with audit quality. The higher the degree of application, the better the audit quality. Deep learning mines nonlinear patterns and hidden risks in financial data through convolutional neural networks and long short-term memory networks, significantly improving the reliability of audit evidence and the accuracy of risk identification. Further analysis shows that internal control quality and industry competition level play a regulatory role in the process of deep learning technology improving audit quality. High-quality internal control and fierce industry competition will weaken the positive impact of deep learning on audit quality. This paper provides empirical support for how digital transformation of enterprises can optimize audit processes through technological innovation, and provides a theoretical basis for regulators to formulate relevant policies.