The Evolution and Future of Big Data Analytics in the Audit Process and Audit Quality Improvement: A Bibliometric Study - Lay, Maria Florensia Cindy Claudia (Pusat Penerbitan dan Publikasi Ilmiah, FEB, Universitas Muslim Indonesia, 2026)
Purpose: The intellectual framework and development of international research on Big Data Analytics (BDA) in the audit process and its effect on audit quality are mapped out in this paper. Research Design and Methodology: A bibliometric analysis was conducted on 773 Scopus documents (2015-2025) using RStudio and Bibliometrix to examine publication trends, key sources, authors, and thematic networks. Findings and Discussion: The analysis shows an annual growth rate of 49.09%, indicating a surge in academic interest. Audit quality is a foundational theme, while machine learning is a motor theme. Big Data Analytics (BDA) enhances audit effectiveness by enabling comprehensive data analysis and more substantial evidence. In risk assessment, BDA combined with AI facilitates proactive identification of high-risk areas through historical and real-time data. In fraud detection, BDA techniques, such as anomaly detection, play a crucial role in identifying suspicious transactions. This transformation shifts the auditor's role into a data-based strategic partner. Implications: The findings underscore the need for dual auditor competencies in accounting and data analytics. Regulators must develop technical-ethical guidelines, and educators should integrate data literacy into their curricula. Future research should include non-English publications and mixed methods.