Autonomous Payroll Intelligence Agent: A Generative AI-Driven Self-Healing Framework for Oracle Cloud HCM Payroll Operations

The payroll activities in Oracle Cloud HCM include very intricate communications between workforce scheduling, time and labor, absence management, compensation and payroll modules. Mantained payroll administration is still very responsive needing a lot of manual intervention before any payroll error, costing discrepancy, lack of timecards, and Fast Formula may be detected and fixed. This research paper postulates an Autonomous Payroll Intelligence Agent (APIA), which is an auto-self-renewable payroll infrastructure based on machine learning, anomaly detection, predictive analytics, and Generative AI to enhance payroll tasks. The payroll transactions within 10,000 payrolls were analyzed in the models of the Logistic Regression, Random Forest, XGBoost, and Isolation forest to obtain their results. Experimental findings revealed high predictive checks on payroll failure, efficient checking of anomalies, and better handling of visibility by elucidate AI methods. The framework also uses Generative AI in root cause analysis and recommendations of corrective actions. The outcome shows significant decreases in the number of errors in payroll, manual interventions, compliance, and the time of resolution. The APIA offers a pathway towards practical.