Cloud-Native AI Framework for Fraud Detection in Telecom Discount Programs

Abstract

The telecommunications industry faces unprecedented challenges in maintaining the integrity of promotional discount programs while combating sophisticated fraudulent activities that exploit large-scale cloud-based systems thus resulting in revenue losses and operational inefficiencies. Traditional rule-based fraud detection systems struggle with the high transaction volumes, low-latency requirements, and evolving attack vectors in modern telecom environments.  This paper presents a cloud-native AI-powered fraud detection framework that shifts from reactive to proactive prevention. Leveraging advanced machine learning algorithms, the system analyzes transactional data, customer behaviors, and contextual information in real-time. These systems employ distributed microservices architectures that support horizontal scaling capabilities and integrate seamlessly with existing telecommunications infrastructures].  Real-time monitoring capabilities enable immediate identification and response to potential fraud attempts through complex event processing engines and automated blocking mechanisms. Case study evaluations demonstrate substantial improvements in fraud detection accuracy, significant reductions in false positive rates, and increased operational efficiency..  This research contributes to cloud computing by demonstrating how AI-driven, cloud-native architectures can deliver secure, scalable, and low-latency fraud detection at telecom scale, offering broader implications for other high-volume, security-sensitive domains.