Towards a Conceptual Framework on the Importance of Privacy and Security Concerns in Audit Data Analytics

Abstract

Privacy and security concerns with Big Data have gained momentum in both research and business fields. With the rapid growth and spread of data, it became difficult for traditional applications to store, compute and analyze huge volume of structured and unstructured data. Processing such data has become possible with the implementation of big data analytics tools. Organizations in general have long recognized the need of data analytics to increase quality and provide better value for financial statements users. Audit firms, in particular, have made significant investments in the field of data analytics, as traditional auditing techniques have not kept pace with the evolving economy and large investments of audit clients in technology. Traditional auditing has not kept pace with the changing economy and the large investments of organizations in data and technology. Audit data analytics have become an increasingly important tool for auditors to increase the quality and performance of their audit. However, such implementation have raised the concerns for privacy and security of client data. The main objective of this paper is to provide a context to the work by highlighting on the security and privacy concerns triggered by big data analytics and the importance of this matter in the audit sector. To this end, using an in-depth literature review, and drawing on a sound theoretical framework comprising utility maximization theory and procedural fairness theory, the paper proposes a conceptual model that depicts the relationships between privacy and security concerns, data analytics and audit quality. The model implications are discussed, and recommendations for future research are presented.