In the fiercely saturated telecom industry, revenue leakage poses a critical threat to profitability. Unidentified billing errors can lead to lost revenue and damage customer confidence. To effectively combat this challenge, telecom providers must implement robust measures to identify and rectify latent revenue leakage points. This demands a combin

SIM box fraud poses a substantial threat to telecom operators worldwide, sapping revenue and compromising network integrity. To effectively combat this illicit activity, a multi-layered approach is essential. This involves implementing robust anti-fraud strategies at multiple stages of the telecom lifecycle. Firstly, operators must fortify their

Telecommunication fraud/theft/deceit is a pervasive problem, costing service providers and consumers billions of dollars annually. Machine learning (ML) offers a powerful arsenal to combat this ever-evolving threat. By analyzing vast datasets of call records, network traffic, and user behavior patterns, ML algorithms can identify/detect/uncover ano