Artificial Intelligence (AI) has rapidly evolved in recent years, revolutionizing various industries. In the field of auditing, AI offers the potential to enhance efficiency, accuracy and risk management. While traditional auditing methods rely heavily on manual processes, AI can automate repetitive tasks, improve data analysis, and detect anomalies more effectively. However, the adoption of AI in auditing is still in its early stages, and challenges such as data quality, ethical considerations, and regulatory compliance need to be addressed. The aim of this work is to provide a comprehensive overview of the state-of-the-art application of digital technologies in audit, with a particular focus on AI. Moreover, the paper aims to outline future developments in this area. The methodology employed in this work involves a comprehensive literature review encompassing both academic research articles and industry reports. Based on this review, the paper is structured into two parts: the first explores the theoretical applications of various technologies in auditing, while the second delves into specific practical cases of AI implementation. Findings show that AI offers significant advantages in auditing, particularly by automating repetitive tasks thus enabling auditors to focus on higher-value activities, such as risk assessment and complex financial analysis. To fully harness its potential, it is crucial to tailor AI solutions to specific auditing activities and to train auditors to utilize AI tools effectively. Furthermore, clear frameworks, rules and organizations are necessary to ensure its ethical and responsible use. Finally, it is essential to acknowledge the risk of systematic errors arising from inherent biases in the data used to train AI models.
Valle et al. (Mon,) studied this question.