Asian Journal of Economics, Business and Accounting

Background: The rapid adoption of digital business models has led to an exponential increase in the volume, velocity, and complexity of data exchanged across interconnected organizational ecosystems. This transformation presents both opportunities and challenges for the auditing profession, as traditional audit approaches struggle to cope with real-time data flows and technologically driven risks. Advances in digitalization—particularly Big Data Analytics (BDA), artificial intelligence (AI), and intelligent automation—are redefining audit processes by enabling continuous assurance, enhanced accuracy, and improved reliability. Within this context, the emerging Audit 5.0 framework emphasizes real-time auditing, intelligent systems, and effective human–AI collaboration as core pillars of modern assurance practices.

Objective: The objective of this study is to examine how digitalization, specifically through the adoption of BDA and AI, influences internal and external auditing within the Audit 5.0 paradigm. The study aims to assess the impact of these technologies on audit quality, risk management, and decision-making, while also examining their implications for auditor roles, competencies, and professional standards, practitioners and regulators within contemporary institutional frameworks. Audit 5.0 presents key challenges related to data quality and integration, the complexity and explainability of advanced technologies, regulatory and ethical uncertainty, and skills shortages combined with cultural resistance within the profession. However, big data analytics and artificial intelligence offer significant opportunities by enabling real-time and predictive risk assessment, expanding audit coverage through full-population testing, improving accuracy, reducing costs and increasing efficiency. Ultimately, Audit 5.0 represents a paradigm shift in auditing, where the effective and responsible use of digital technologies enhances audit quality, strengthens professional judgement, and delivers greater strategic value to stakeholders.

Research Method: The study employs a mixed-method research design combining a systematic literature review with empirical analysis, analytical approach, synthesizing prior academic literature, professional standards and regulatory perspectives to assess the role of BDA and AI in audit processes, including risk assessment, anomaly detection and continuous auditing. The literature review synthesizes prior theoretical and empirical studies on digital auditing, Audit 5.0, BDA, and AI. Empirical data are analyzed using structural equation modeling and relevant statistical tests to assess relationships among digitalization, audit processes, and audit outcomes, with particular focus on organizations in the finance and technology sectors.

Research Result: The findings indicate that audits supported by BDA and AI significantly outperform traditional audit approaches. The results further reveal that digitalization improves audit productivity, facilitates continuous auditing, strengthens data security, and enhances stakeholder trust. With consistent empirical evidence that AI investment correlates with reductions in audit restatements and improved efficiency, these technologies can transform audit practices by enabling real-time and predictive risk assessment and enhanced fraud detection, thereby expanding audit coverage and accuracy beyond traditional sampling method. There are need for stronger governance, ethical frameworks and targeted training to fully realize the benefits of digital auditing. Overall, the evidence confirms that integrating BDA and AI within the Audit 5.0 framework represents a fundamental shift toward intelligent, adaptive, and value-driven auditing, while underscoring the need for enhanced auditor competencies and alignment with evolving regulatory and professional requirements.