Data-Driven Approaches to Fraud Detection in Telecommunication Networks

Abstract

As telecommunication networks expand, fraudulent schemes—ranging from subscription fraud and identity theft to premium-rate abuse—pose growing threats to service providers. This paper presents a suite of data science strategies for detecting and mitigating fraud in telecom environments, integrating machine learning classifiers, statistical modeling, and real-time anomaly detection within scalable streaming architectures. We evaluate both supervised methods (e.g., decision trees, gradient boosting machines, neural networks) and unsupervised approaches (e.g., clustering algorithms, autoencoders), assessing their detection accuracy, false-positive rates, and computational efficiency on large-scale call-detail-record datasets. Our results show that ensemble models built with domain-specific feature engineering deliver superior precision and recall, while unsupervised techniques are particularly effective at surfacing novel fraud patterns—albeit with greater tuning requirements to prevent overfitting. We also address operational challenges such as data privacy, model drift, and latency constraints, and outline best practices for integrating these solutions into live telecom infrastructures. Finally, we explore emerging trends—deep learning architectures, explainable AI frameworks, and blockchain-based identity verification—and discuss their potential to fortify next-generation fraud prevention systems in telecommunication networks.