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Artificial Intelligence (AI)-Based Advance Models for Proactive Payroll Fraud Detection and Prevention

Sunil Jacob Enokkaren, Raghuvaran Kendyala, Jagan Kurma, Jaya Vardhani Mamidala, Varun Bitkuri, Avinash Attipalli

DOI: 10.63665/ijmlaidse-y1f1a001

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Abstract

Payroll fraud remains one of the most significant financial threats to organizations, directly impacting financial stability and institutional trust. Traditional rule-based detection mechanisms are often inadequate in addressing evolving fraud patterns. With increasing digitization of payroll systems and integration into ERP platforms, the complexity of detecting anomalies has intensified. This paper explores Artificial Intelligence (AI) and Machine Learning (ML)-based models for proactive payroll fraud detection and prevention. The study examines supervised, unsupervised, reinforcement learning, and deep learning techniques, highlighting their ability to detect anomalies, predict fraudulent activities, and enhance decision-making accuracy. Challenges including data privacy, explainability, and system integration are also discussed.

Keywords

Payroll Fraud, Fraud Detection, Artificial Intelligence, Machine Learning, ERP Systems, Anomaly Detection, Financial Security, Deep Learning

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