REAL-TIME DETECTION OF INTERCONNECT BYPASS FRAUD IN TELECOMMUNICATION NETWORKS: CAMEL FRAMEWORK LOW-CODE APPROACH AND AI/ML ADAPTATION

DOI: 10.31673/2409-7292.2025.031728

Abstract

Interconnect Bypass Fraud poses a significant threat to telecommunication operators, leading to substantial
revenue losses and degraded service quality. This fraud involves routing calls through unauthorized, low-cost channels,
bypassing legitimate interconnect agreements. Traditional detection methods often rely on offline or near real-time
analysis, which may not suffice for timely mitigation.
This article proposes a real-time detection solution leveraging the CAMEL framework, enhanced by a low-code
development approach and AI/ML integration. The solution aims to provide flexibility, rapid adaptation, and high
accuracy in fraud detection while minimizing the need for deep programming expertise. By combining signaling protocol
analysis (CAP/IMS_CAP/INAP) with AI-driven anomaly detection, the proposed system addresses both current and
emerging fraud techniques. The article also explores the adaptation of AI/ML within the low-code software lifecycle to
further optimize fraud detection workflows.
Keywords: Online interconnect bypass fraud detection, signaling, call-control, low-code, artificial intelligence,
machine learning, information security.

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