With the rising expectations for transparency and efficiency in the public sector, Artificial Intelligence (AI) and Machine Learning (ML) have become pivotal technologies in modern auditing practices. By automating routine tasks, improving data analysis, and enhancing risk assessment, AI and ML can help auditors become more efficient, effective, and accurate in their work. This article explores the historical context and the integration of AI and ML into public sector auditing, highlighting real-world applications and the implications for improving efficiency, accuracy, and decision-making. AI and ML offer significant opportunities, such as improved fraud detection and real-time monitoring. However, they also present challenges like data quality issues and cultural resistance. Additionally, this article examines the role of the Asian Organization of Supreme Audit Institutions (ASOSAI) in promoting the adoption of AI and ML. Leveraging his expertise, the author addresses these challenges and harnesses the full potential of these technologies to ensure the effective use of public resources. This article seeks to offer a thorough examination of the present state, potential future trends, and the essential equilibrium between innovation and ethical considerations in public sector auditing.

The auditing profession has undergone substantial changes over the years, with technological advancements playing a crucial role. Traditional auditing methods often involved manual reviews and spot checks, which were time-consuming and prone to human error. The introduction of AI and ML has revolutionised these practices, enabling real-time data analysis, continuous monitoring, and predictive analytics.

Public sector auditingplays a crucial role inensuringtransparency andeffectivegovernance. Inanage characterized by rapid technological advancements, the public sector is undergoing significant transformation. Among these changes, the integration of AI and ML into auditing processes stands out as a critical development. As governmental organisations strive to enhance their operational efficiency, AI and ML offer significant opportunities as well as considerable challenges in auditing.

The potential for these technologies to enhance the efficiency and effectiveness of audits has garnered attention from governments and auditing organisations worldwide. AI refers to the ability of machines to perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, and making decisions. ML, a subset of AI, focuses on algorithms that enable systems to learn from data and improve over time. Therefore, AI and ML, by definition, refer to the development of intelligent systems capable of learning, reasoning, problem-solving, and perception

In the context of auditing, these technologies can be leveraged to enhance efficiency, accuracy, and effectiveness. By automating routine tasks, analysing vast datasets, and identifying patterns and anomalies, AI and ML can empower auditors to focus on higher-value activities, uncover hidden risks, and provide more insightful recommendations. The development of AI and ML technologies has progressed significantly in recent years, driven by advances in computational power and the availability of large datasets.

This article aims to explore how AI and ML can revolutionise public sector auditing by leveraging innovative technologies to improve accuracy, transparency, and decision-making. It will also discuss the challenges associated with adopting these technologies, such as data privacy, ethical concerns, and the need for auditor training. Additionally, the role of ASOSAI in promoting the adoption of AI and ML among its member countries will be examined. Overall, this article assesses the opportunities and challenges of using AI and ML in public sector auditing, drawing on recent research and practical applications to underscore their implications for audit quality and governance.

The incorporation of AI and ML into public sector auditing presents several promising opportunities. One of the most significant benefits is the enhancement of efficiency and effectiveness. By automating time-consuming tasks such as data extraction, classification, and analysis, AI and ML allow auditors to focus on strategic activities that require critical thinking. For instance, these technologies can extract data from various sources, identify anomalies in financial data, and generate audit reports, leading to significant reductions in audit cycle times and improved overall efficiency.

Moreover, AI and ML algorithms offer improved accuracy by analysing vast datasets with greater precision than human auditors. They can effectively identify patterns and discrepancies that may indicate fraud or inefficiency, thus reducing the risk of human error and enhancing the overall effectiveness of audits. This is complemented by real-time monitoring and continuous auditing practices, which facilitate immediate detection of anomalies or potential fraud, allowing for timely interventions.

In addition to improving efficiency and accuracy, AI and ML can ensure compliance with regulations and policies through continuous monitoring of transactions and processes. These technologies provide data-driven insights that inform decision-making, helping auditors prioritise their work and allocate resources more effectively. By analysing large datasets, AI and ML can uncover trends, correlations, and causal relationships that may not be immediately apparent, further improving audit quality.

Causal knowledge discovery is another critical opportunity presented by AI and ML. The author’s findings can help auditors understand the underlying factors contributing to financial discrepancies, ultimately enhancing audit quality. For instance, the methodology developed for predictive modeling in risk management could be adapted to public sector auditing, demonstrating how identifying causal relationships can lead to more accurate assessments and better decision-making.

Additionally, AI and ML revolutionize risk identification and assessment through predictive analytics. By analyzing historical data and identifying unusual patterns or inconsistencies, auditors can proactively detect potential risks and take preventative measures, such as identifying fraudulent activities in transaction data.

The technologies also contribute to improved transparency in the public sector by enhancing oversight and detecting irregularities, ensuring that public funds are used effectively and efficiently. AI-powered systems can produce detailed and informative reports that enhance the transparency of auditing processes, ultimately increasing public trust in government institutions. Furthermore, the automation of audit processes significantly reduces costs associated with manual audits. To summarize, AI and ML opportunities in public sector auditing can be concluded as:

• Automated Compliance.
• Causal Knowledge Discovery.
• Fraud and Anomaly Detection.
• Data-Driven Decision-Making.
• Predictive Analytics for Risk Management.
• Cost Reduction and Resource Optimization.
• Real-Time Monitoring and Continuous Auditing.
• Improved Accuracy, Efficiency and Transparency.

Despite the numerous benefits, the adoption of AI and ML in public-sector auditing presents several challenges. While these challenges are significant, several strategies and best practices can help mitigate these issues and enhance the adoption process. Below are the challenges and best practices for implementing AI and ML in auditing: