Building AI-Driven Payroll Systems: Microservice Framework for Configurable Anomaly Detection and Real-Time Alerts

John Selvaraj Arulappan

Abstract: In the era of digital transformation, payroll systems play a critical role in ensuring accurate and efficient financial operations for organizations. However, these systems are prone to anomalies such as erroneous payments, fraudulent activities, and compliance violations, which can lead to significant financial and reputational risks. This paper presents a robust design framework for integrating AI-driven anomaly detection into payroll systems to optimize accuracy, efficiency, and security. By embedding anomaly detection into existing payroll automation frameworks, organizations can achieve proactive monitoring, rapid anomaly resolution, and enhanced decision-making capabilities. This study also addresses key challenges such as scalability, data quality, and integration complexities, while highlighting ethical considerations like data privacy and bias mitigation.

Keywords: Payroll processing Engine, Open AI, Anomaly Detection, Microservice