From Anomalies to Alerts: A Survey of AI-Driven Real-Time Payroll Risk Detection Systems
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I3P118Keywords:
Payroll Systems, Payroll Anomaly Detection, Artificial Intelligence, Payroll Fraud Detection, Risk Scoring, Hr Analytics, Payroll GovernanceAbstract
Payroll systems form an important part of the operations of organizations but with their growing digitalization, they have become vulnerable to multifaceted dangers like frauds, manipulation of data, failure to comply with rules, and mistakes during operations. The manual and rule-based payroll controls do not usually suffice to identify insidious or changing anomalies in big data and real-time payroll conditions. The paper proposes an overall structure on how to identify payroll anomalies and risks through the implementation of AI and ML in the current payroll systems. It explores the background to the sources of payroll data, fraud types frequently used, and the adoption issues that apply to digital payroll platforms. The paper also compares supervised, unsupervised and deep learning-based methods of payroll anomaly detection and identifies their strengths and weaknesses in comparison with one another. The proposed solution is capable of providing real-time risk intelligence and ongoing fraud prevention by shifting the mode of detection of anomalies to risk scoring, severity evaluation and prioritized alerting. The results establish AI-based payroll surveillance as more accurate, efficient in its operations, compliant, and governable and less prone to financial losses and organizational risk. The paper highlights how intelligent payroll systems have enhanced decision support, payroll integrity and sustainable and transparent HR operations.
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