Implementing Big Data Auditing: Pathways and Institutional Safeguards for Effective Adoption

Abstract

In the digital era, big data auditing has gradually emerged as a crucial approach in audit work, boasting its distinct advantages. This paper delves into the implementation pathways and safeguard mechanisms that facilitate the advancement of big data auditing. Initially, it elaborates on the theoretical essence of big data auditing and its alignment with the theory of comprehensive audit coverage. Subsequently, through empirical investigation, it demonstrates the application effects of big data analysis technology in auditing, encompassing trend analysis, cluster analysis, anomaly detection, correlation analysis, and machine learning prediction technology. The research findings indicate that big data auditing can effectively enhance the ability to identify problems in auditing and achieve comprehensive audit coverage. In terms of implementation pathways, suggestions are put forward, such as strengthening data governance, improving technical capabilities, optimizing audit procedures, and establishing coordination mechanisms. Regarding safeguard mechanisms, emphasis is placed on the significance of organizational guarantees, system guarantees, talent guarantees, and security guarantees. Despite the fact that big data auditing still encounters numerous challenges in practical applications, it plays a vital role in promoting the healthy development of the economy and society and safeguarding national economic security. In the future, efforts should be made to further improve the implementation pathways and safeguard mechanisms to promote the sustainable development of big data auditing.