AI-Based Fraud Detection in the Telecom Sector (A Comprehensive Study on Applying Machine Learning and Artificial Intelligence to Detect Fraud in Telecommunications)

Ahmad Khamees Ibrahim Al-Betar () and Mahmoud Amjed Mohammad Alameiri ()

International Journal of Innovative Science and Research Technology (IJISRT), 2025, vol. 10, issue 12, 84-87

Abstract: Fraud remains a critical operational and financial challenge within the telecommunications sector, where subscription manipulation, SIM cloning, spoofing, and usage anomalies contribute to significant revenue leakage. Traditional rule-based detection systems are increasingly inadequate due to evolving fraud patterns and sophisticated attack strategies. This research investigates the effectiveness of artificial intelligence (AI)-driven fraud detection models in enhancing telecom security resilience and operational responsiveness. Using the Saudi Telecom Company (STC) as a case reference, the study evaluates how machine learning, anomaly detection, and real-time analytics improve the ability to identify fraudulent transactions and reduce response time. Through a qualitative review of industry practices and comparative analysis of AI-based systems, the findings highlight that predictive modeling and automated monitoring substantially strengthen fraud detection accuracy while reducing manual investigation overhead. The research concludes that AI is a strategic enabler for telecom fraud prevention, provided sufficient investment is made in data integration, algorithm training, and governance readiness.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cvr:ijisrt:2025:12:ijisrt25dec072

DOI: 10.38124/ijisrt/25dec072

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