The Use of Big Data in Analytical Audit Procedures: A Literature Review

Purpose: This study aims to analyze how Big Data and Big Data Analytics (BDA) are used to strengthen analytical audit procedures in accordance with ISA 520, while identifying the benefits, implementation challenges, and research development directions. Research Method: The study used a Systematic Literature Review (SLR) approach with the PRISMA 2020 guidelines. The identification, selection, and thematic synthesis processes were carried out for 10 articles on Big Data, analytical procedures, and audits in accordance with professional standards. Results and Discussion: Findings show that BDA strengthens analytical procedures at all stages of the audit, namely planning, substantive testing, and closing. Big data analytics enables comprehensive population analysis, more accurate expectations, and more precise anomaly detection than traditional techniques. Big Data can improve audit quality by enabling more accurate risk identification and greater process efficiency, potentially reducing audit delays. However, implementation faces obstacles related to auditor competence, infrastructure readiness, varying data quality, and privacy and information security risks. Implications: This study clarifies the mechanism for integrating Big Data into analytical procedures in accordance with ISA 520. It supports the need for technical guidelines, analytical models, and the strengthening of auditors' digital audit capabilities.