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stc details in a a case study how it is enhancing revenue assurance through AI-driven fraud management.
How stc integrated machine learning into operational fraud detection
stc (Saudi Telecom Company) is strengthening its fraud management and revenue assurance (RAFM) capabilities by embedding artificial intelligence (AI) and machine learning (ML) into its operational environment.
As fraud techniques become increasingly sophisticated and transaction volumes grow, traditional rule-based controls alone can face challenges in identifying emerging fraud patterns.
To address this, stc integrated ML models into its existing RAID-FMS platform, combining AI-driven analytics with established rule-based controls, behavioral analytics, anomaly detection, and large-scale traffic analysis.
Three ML-enabled use cases—IRSF, Bypass, and Wangiri—were deployed through a phased approach covering assessment, model development and validation, platform integration, and operational deployment.
Initial operational results indicate approximately 15% improvement in fraud detection effectiveness, 7% reduction in revenue leakage, 35% earlier identification of fraud incidents, and 20% reduction in investigation effort.
Building on its digital transformation journey and previous TM Forum conformance experience in application operations, stc aligned its operational practices with TM Forum frameworks, particularly eTOM.
This initiative demonstrates how combining AI with established operational controls can strengthen revenue protection, improve operational efficiency, and support the evolution of AI-driven Business Assurance and intelligent operations.
