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Data Asset Auditing: Risks and Challenges
Abstract
This study investigates the audit risks associated with data assets, which have become increasingly vital in the digital economy. As data assets differ significantly from traditional assets due to their intangibility, replicability, and complex valuation processes, traditional audit frameworks are inadequate for identifying and assessing the specific risks they pose. The research highlights the necessity of establishing a specialized audit framework tailored to data assets, cultivating multidisciplinary audit professionals, and adopting innovative audit technologies such as blockchain, AI, and RPA. These measures are essential for ensuring the accuracy, compliance, and security of data assets in financial statements. The study also addresses the importance of effective governance and management of data assets, emphasizing the role of internal controls and the potential risks of significant misstatements due to errors or fraud. The findings provide valuable insights for auditors and accounting firms, guiding them in adapting their practices to meet the challenges presented by data assets, thus safeguarding the integrity of financial reporting in the digital era.
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