This research study examines incorporation of an AI based anomaly detection method of Oracle cloud Payroll to make the payroll more accurate, reduce risks, and enhance compliance. The performance of different machine learning models such as Isolation Forest, One-Class SVM, Neural Networks, and Logistic Regression is tested in terms of their performance in detecting payroll data anomalies. Findings indicate that models such as Logistic Regression performed moderately well but other models did not cope with false positives and poor anomaly detection. The future is to perfect the models, involve deep learning, and realize the real-time anomaly to make the payroll management in large organizations more efficient and accurate.