Exploring the potential impact of big data on the collection of sufficient, appropriate audit evidence: insights from auditors in the UAE

Document Type

Article

Source of Publication

Qualitative Research in Financial Markets

Publication Date

1-1-2024

Abstract

Purpose: This paper aims to understand the impact of big data on the UAE audit profession. Mainly exploring whether the emergence of big data threatens the reliability of audit standards and whether audit standards need to be improved. Also, exploring the impact of big data on the collection of audit evidence. Design/methodology/approach: Semistructured interviews were used to collect data, mainly targeting the audit-related workers of the Big Four and Non-Big Four audit firms in the UAE. Thematic analysis is adopted to analyze the original data, and the main factors affecting the audit standard and audit evidence collection. Findings: This study found that the reliability of audit standards and the way audit evidence is collected can be affected by big data. It concludes that audit standards need to be improved and strengthened to include detailed essential elements associated with big data to ensure audit reliability, legitimacy and regularity. The results also identify the impact of big data on audit evidence in terms of adequacy, appropriateness, authenticity, consistency and reliability, as well as the impact on the validity and completeness of evidence collection. The research highlights the importance of big data skills and knowledge education, the contribution and challenges of big data to auditing, and the use of big data in future auditing. Originality/value: This research provides specific empirical evidence from both Big Four and Non-Big Four audit firms in the UAE, which is lacking in the literature on the use of big data technology by auditors to assist audit works in UAE. It may serve as a reference for other researchers or those interested in relevant research.

ISSN

1755-4179

Publisher

Emerald

Disciplines

Business

Keywords

Audit evidence, Audit evidence collection, Audit standard, Audit standard compliance, Auditor, Big data, Big data analytics

Scopus ID

85203017919

Indexed in Scopus

yes

Open Access

no

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