Research on Audit Risks in the Recognition of Data Assets on Financial Statements

This paper explores the audit risks associated with the recognition of data assets on financial statements, focusing on the complexities arising from their replicability, unique valuation patterns, and contextual dependencies. It identifies major misstatement risks at both the financial statement and assertion levels, including the potential for management to exaggerate data asset values, uncertainties in valuation methods, and deficiencies in data governance and internal controls. Additionally, auditors’ lack of professional knowledge and inappropriate audit methods can lead to inspection risks. The paper emphasizes the urgent need for enhanced accounting standards for data assets, effective guidelines for their recognition and measurement, and robust internal controls. Furthermore, it advocates for the exploration of effective valuation methods and the incorporation of advanced technologies, such as big data and AI, into auditing practices. By improving auditor training and methodologies, organizations can better manage the inherent risks associated with data asset auditing.