The article "Enhancing Audit Accuracy: The Role of AI in Detecting Financial Anomalies and Fraud" by Beatrice Oyinkansola Adelakun, Bernard Owusu Antwi, Damilola Temitayo Fatogun, and Omolara Patricia Olaiya explores the impact of Artificial Intelligence (AI) on audit accuracy. The authors highlight how AI, particularly machine learning algorithms and advanced data analytics, can significantly enhance the detection of financial anomalies and fraud. AI-driven auditing tools can process large datasets quickly, identifying patterns and deviations that may indicate fraudulent activities or errors. Unlike traditional methods, which rely on sampling and manual checks, AI can evaluate entire datasets, ensuring comprehensive coverage and reducing the likelihood of undetected issues. The paper discusses the benefits of AI in enhancing anomaly detection, including its ability to recognize subtle or complex fraud patterns and its predictive capabilities in identifying potential risks. However, it also addresses the challenges associated with implementing AI in auditing, such as ensuring data quality and integrity, addressing algorithmic biases, and maintaining transparency in AI decision-making processes. Despite these challenges, the authors conclude that AI holds significant promise for improving audit accuracy and reliability, ultimately strengthening financial oversight and integrity.The article "Enhancing Audit Accuracy: The Role of AI in Detecting Financial Anomalies and Fraud" by Beatrice Oyinkansola Adelakun, Bernard Owusu Antwi, Damilola Temitayo Fatogun, and Omolara Patricia Olaiya explores the impact of Artificial Intelligence (AI) on audit accuracy. The authors highlight how AI, particularly machine learning algorithms and advanced data analytics, can significantly enhance the detection of financial anomalies and fraud. AI-driven auditing tools can process large datasets quickly, identifying patterns and deviations that may indicate fraudulent activities or errors. Unlike traditional methods, which rely on sampling and manual checks, AI can evaluate entire datasets, ensuring comprehensive coverage and reducing the likelihood of undetected issues. The paper discusses the benefits of AI in enhancing anomaly detection, including its ability to recognize subtle or complex fraud patterns and its predictive capabilities in identifying potential risks. However, it also addresses the challenges associated with implementing AI in auditing, such as ensuring data quality and integrity, addressing algorithmic biases, and maintaining transparency in AI decision-making processes. Despite these challenges, the authors conclude that AI holds significant promise for improving audit accuracy and reliability, ultimately strengthening financial oversight and integrity.