Challenges and Limitations of Traditional Financial Auditing in the Big Data Environment

Abstract

Driven by the digital wave, big data technology has profoundly transformed enterprise operation models, with massive amounts of structured, semi-structured, and unstructured data emerging continuously. As a long-standing economic supervision activity, traditional financial auditing faces severe challenges in data acquisition and processing, auditing methods and technologies, auditor capabilities, and audit risk control. This paper explores the limitations of traditional financial auditing in the big data environment through literature research, case analysis, and comparative analysis. The study finds that traditional auditing methods struggle to meet the demands of the big data era in terms of data processing speed, audit accuracy, and risk control. To address these challenges, this paper proposes recommendations such as promoting auditing technology innovation, strengthening auditor training, improving auditing standards and norms, and establishing a data security management system. These measures aim to drive the transformation and upgrading of traditional financial auditing, enhance the quality and efficiency of auditing work, and better enable auditing to play its important role in maintaining market economic order and protecting investors' rights and interests.