AI-Driven Fraud Detection in Telecommunication Billing Systems

Telecommunications fraud is one of the major challenges for operators, since it generates significant losses in revenues and causes dissatisfaction among customers. Traditional detection methods often cannot solve the problem of dynamic and ever-changing fraud activities. This paper proposes an advanced architecture for a fraud detection system, using artificial intelligence and machine learning models like LightGBM, which could improve fraud detection accuracy and efficiency. Core functionalities like data gathering, processing, forecasting, and feedback are brought together under a module-based framework to enhance scalability and flexibility. Distributed computing platforms, cloud platforms, and realtime analytics solutions are leveraged in the system to handle huge sets of data in a way that is sensitive to privacy policy. Testing shows that the architecture provides high accuracy and efficiency of operations, thus representing a promising solution for real-world challenges in telecommunications fraud. Further potential developments, like the introduction of self-learning systems and blockchain technologies, are a sign of its future adaptability and efficiency.