A Comparative Study of Machine Learning Approaches for Fraud Detection in Telecommunication Networks

DOI

10.2991/978-94-6239-697-5_3How to use a DOI?

Keywords

Machine Learning; Artificial Intelligence; Fraud Detection; Anomaly Detection; Telecommunication Networks

Abstract

As the telecommunication technologies continue to expand, they have brought about many opportunities in terms of global connectivity, yet they have equally enhanced the chances of other frauds like phishing, SMiShing, voice spam and revenue share fraud. The old systems of detection, which rely on fixed rules and manual inspection, are becoming ineffective against new, changing and unknown threats. The current review explores the published articles within the past year 2018–2025 concerning the use of the techniques of Machine Learning (ML) and Artificial Intelligence (AI) in enhancing the process of detecting telecom fraud. The analysis is based on supervised, unsupervised, and hybrid ML models, such as Random Forest, SVM, Gradient Boosting, and LightGBM, and the literature on the issue of data imbalance and privacy concerns. Findings in the reviewed literature indicate that systems based on ML have the ability to attain accuracy rates of over 95 percent and can help in real-time detection. The review also highlights the existing challenges like the scarcity of public datasets, non-explainable, and ethical issues. On the whole, this paper highlights the increasing use of ML in the development of smart, expandable, and secure fraud detection systems in next-generation telecom networks.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Divya Sharma
AU  - Satnam Kaur
AU  - Divya Bansal
AU  - Mamta Dabra
PY  - 2026
DA  - 2026/06/04
TI  - A Comparative Study of Machine Learning Approaches for Fraud Detection in Telecommunication Networks
BT  - Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)
PB  - Atlantis Press
SP  - 12
EP  - 24
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-697-5_3
DO  - 10.2991/978-94-6239-697-5_3
ID  - Sharma2026
ER  -