AI and ML in payroll automation: A technical perspective

Meenugu, Sadanandam (2025) AI and ML in payroll automation: A technical perspective. World Journal of Advanced Engineering Technology and Sciences, 15 (1). pp. 1542-1552. ISSN 2582-8266

Artificial Intelligence and Machine Learning technologies are fundamentally transforming payroll management across global organizations, moving beyond basic automation toward intelligent systems capable of learning and optimization. These advanced computational approaches address traditional payroll challenges including error reduction, compliance management, and processing efficiency across diverse regulatory environments. The article explores the technical architecture underlying AI-powered payroll systems, examining the multi-layered frameworks that enable sophisticated data processing and decision support. Core machine learning algorithms—including regression models, classification algorithms, anomaly detection systems, natural language processing, and reinforcement learning—are revolutionizing specific payroll functions such as predictive analytics, tax calculation, error detection, and personalized insights. Despite significant implementation challenges related to data quality, security considerations, and explainability requirements, organizations are developing innovative solutions through federated learning, differential privacy, and model interpretation techniques. Looking forward, emerging technologies including blockchain and quantum computing promise to further revolutionize payroll operations through smart contracts, immutable transaction records, enhanced tax optimization, and global workforce management capabilities.