AI-Driven and Data-Intensive Auditing: Enhancing Sustainability and Intelligent Assurance

Abstract

The integration of artificial intelligence (AI) and big data analytics has revolutionized audit practices, offering unprecedented advancements in efficiency, transparency, and sustainability. This study critically examines the role of AI-powered auditing in risk detection, anomaly identification, and the development of sustainable audit frameworks. Through an extensive literature review, the adoption of machine learning (ML), natural language processing (NLP), and continuous auditing methodologies is explored, highlighting their impact on audit quality and assurance. It is demonstrated that AI-driven auditing significantly enhances error detection and risk assessment while expediting audit procedures and improving overall accuracy. However, critical challenges remain, including data security risks, algorithmic opacity, and ethical concerns related to decision-making autonomy. Addressing these issues necessitates the establishment of robust governance mechanisms, increased algorithmic transparency, and the implementation of continuous professional training programs to ensure auditors' proficiency in AI-based systems. Furthermore, AI-driven automation enables real-time monitoring and predictive analytics, fostering a proactive approach to auditing that mitigates financial and operational risks. By leveraging AI and data-driven methodologies, audit frameworks can be rendered more adaptive, resilient, and aligned with the evolving digital economy. These findings underscore the necessity for organizations to integrate AI-driven auditing as a strategic imperative while ensuring regulatory compliance and ethical oversight.