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Machine Learning Models for Fraud Detection in Promotional Activities

Machine Learning Models for Fraud Detection in Promotional Activities
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Author(s): Munir Ahmad (Survey of Pakistan, Islamabad, Pakistan), Kanchon Kumar Bishnu (California State University, Los Angeles, USA), Md Rasibul Islam (Gannon University, Buffalo, USA)and Md Khalilor Rahman (Gannon University, Hermitage, USA)
Copyright: 2026
Pages: 30
Source title: Harnessing AI for Point-of-Sale Optimization
Source Author(s)/Editor(s): Nozha Erragcha (University of Jendouba, Tunisia)and Maher Toukabri (Northern Border University, Saudi Arabia & University of Jendouba, Tunisia & Laboratory ARBRE, University of Tunis, Tunisia)
DOI: 10.4018/979-8-3373-4392-1.ch009

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Abstract

This chapter explores the application of machine learning (ML) techniques for detecting and preventing fraud in digital marketing and promotional campaigns. It examines common fraud types, including click fraud, coupon abuse, identity manipulation, and network-based schemes, and reviews supervised, unsupervised, and deep learning approaches for anomaly detection. Key challenges, such as data quality, model interpretability, real-time processing, and ethical considerations, are discussed. Emerging trends, including predictive AI, hybrid models, and graph-based analytics, highlight future directions for robust, adaptive, and trustworthy promotional fraud detection systems.

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