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

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.