Optimization of Enterprise Accounting Audit Risk Identification and Prevention Strategy Based on Machine Learning

Abstract

In an era of rapid technological advancement and increased regulatory scrutiny, optimizing enterprise accounting audit risk identification and prevention strategy is critical for organizations looking to protect financial integrity and ensure regulatory compliance. Traditional audit risk management systems frequently fail to keep up with the intricacies of current corporate transactions, resulting in gaps in risk coverage and potential compliance violations. In response to these problems, this study analyzes the use of machine learning techniques in business accounting audit processes to improve risk management effectiveness and efficiency. This project aims to create specialized approaches and models that use machine learning algorithms to efficiently identify, assess, and mitigate audit risks in real time. This study intends to highlight the benefits, challenges, and best practices of using machine learning in audit risk management through empirical analysis, evaluation, and practical insights. This study adds to the continuing discussion about the future of auditing in the digital era, which is set within the larger framework of technical innovation and digital change within the accounting profession. This study intends to assist businesses by providing a roadmap for enterprises wanting to optimize their audit risk management procedures using machine learning.