Artificial Intelligence (AI) is transforming auditing by significantly improving the detection of financial anomalies and fraud. AI technologies, such as machine learning algorithms, enable auditors to analyze vast datasets quickly and accurately, identifying patterns and anomalies that may indicate fraudulent activities or errors. This review explores the impact of AI on audit accuracy, focusing on its role in identifying irregularities and fraudulent activities. AI-driven auditing tools leverage machine learning and advanced data analytics to scrutinize financial records with high detail, processing large amounts of data rapidly to detect deviations that may signal anomalies or fraud. Unlike traditional audit techniques, which rely on sampling and manual checks, AI can evaluate entire datasets, ensuring comprehensive coverage and reducing the likelihood of undetected issues. AI enhances anomaly detection by training machine learning models to recognize normal financial behaviors and flag deviations that may warrant further investigation. This capability is particularly valuable in identifying subtle or complex fraud patterns that might be missed by human auditors. AI can detect unusual transaction patterns, inconsistencies in financial statements, or irregularities in vendor or customer behaviors, which are common indicators of fraud. Additionally, AI's predictive analytics can proactively identify potential risks by analyzing historical data and forecasting future trends, allowing auditors to anticipate areas of concern and allocate resources more effectively. AI systems continuously learn and adapt, enhancing their accuracy and reliability over time. However, challenges such as ensuring data quality, addressing algorithmic biases, and maintaining transparency in AI decision-making processes must be carefully managed. Auditors must stay updated with evolving AI technologies and regulatory requirements to maximize benefits while mitigating risks. AI has the potential to revolutionize auditing by improving the detection of financial anomalies and fraud, leading to more thorough and reliable audits. By integrating AI into auditing practices, organizations can enhance financial oversight and integrity. Despite its advantages, careful management of associated challenges is essential to fully realize AI's potential in auditing.Artificial Intelligence (AI) is transforming auditing by significantly improving the detection of financial anomalies and fraud. AI technologies, such as machine learning algorithms, enable auditors to analyze vast datasets quickly and accurately, identifying patterns and anomalies that may indicate fraudulent activities or errors. This review explores the impact of AI on audit accuracy, focusing on its role in identifying irregularities and fraudulent activities. AI-driven auditing tools leverage machine learning and advanced data analytics to scrutinize financial records with high detail, processing large amounts of data rapidly to detect deviations that may signal anomalies or fraud. Unlike traditional audit techniques, which rely on sampling and manual checks, AI can evaluate entire datasets, ensuring comprehensive coverage and reducing the likelihood of undetected issues. AI enhances anomaly detection by training machine learning models to recognize normal financial behaviors and flag deviations that may warrant further investigation. This capability is particularly valuable in identifying subtle or complex fraud patterns that might be missed by human auditors. AI can detect unusual transaction patterns, inconsistencies in financial statements, or irregularities in vendor or customer behaviors, which are common indicators of fraud. Additionally, AI's predictive analytics can proactively identify potential risks by analyzing historical data and forecasting future trends, allowing auditors to anticipate areas of concern and allocate resources more effectively. AI systems continuously learn and adapt, enhancing their accuracy and reliability over time. However, challenges such as ensuring data quality, addressing algorithmic biases, and maintaining transparency in AI decision-making processes must be carefully managed. Auditors must stay updated with evolving AI technologies and regulatory requirements to maximize benefits while mitigating risks. AI has the potential to revolutionize auditing by improving the detection of financial anomalies and fraud, leading to more thorough and reliable audits. By integrating AI into auditing practices, organizations can enhance financial oversight and integrity. Despite its advantages, careful management of associated challenges is essential to fully realize AI's potential in auditing.