Anomaly Detection System: Machine Learning
Industry
Telecommunications
Project
Route Trust Core
Client
Route Trust
Our Role
Development
Technologies
NODE JS, Tensor Flow
" CloudTern consistently demonstrates expertise in building and maintaining AI models, showcasing unwavering commitment to upholding high standards of professionalism. Their steadfast dedication underscores CloudTern's firmly established reputation for reliability and proficiency."
David Shifley
Chief Technology Officer
Company Overview
RouteTrust, an IT service based company, provides cutting-edge softswitch, Resporg, and Least Cost Routing services, complemented by integrated reporting solutions. Amidst evolving telecom regulations and industry consolidation, we equip carriers to navigate profitably. Our innovative software solutions empower clients to meet current goals while preparing for future demands, ensuring stability and scalability in the dynamic VoIP landscape. Experience heightened efficiency and profitability with RouteTrust’s specialized technology expertise, particularly in LCR for modern communication firms.
The Challenge
To build and train an AI/ML model upon Call Detail Records (CDR), leveraging data extracted and summarized by the Rt/Vue product. To ensure CDR information by various parameters like Call Date, Hour, Minute, Call Type, dial code, and other essential metrics, harmonizing them into unified pivot points. To ingesting data from potentially 35 diverse sources, meticulously transforming them into a standardized format through Extract, Transform, Load (ETL) processes.
The Solution
Our analytics platform utilizes metrics like CDRs, ASR, CPS, and Port Utilization for insights into network dynamics via AI/ML model. ASR summarizes CDRs, CPS monitors call traffic, and Port Utilization tracks network usage patterns. Financial analysis dissects revenue and costs by customer/vendor, while number analysis combines ASR and financial data for network performance evaluations.
Our solution analyzes Ingress and Egress activities to reveal communication trends, enabling actionable insights for network optimization. Leveraging ASR, financial analysis, and comprehensive number analysis, we offer a holistic view of network performance. This facilitates informed decision-making and enhances operational efficiency.
Key Benefits
Anomaly Detection
ML algorithms can detect unusual patterns or anomalies in CDR data, such as fraudulent activities or abnormal call behaviors. This helps in identifying and mitigating security threats or fraudulent activities in real-time.
Cost Reduction
By automating various tasks such as fraud detection, network optimization, and customer segmentation, ML models help telecom operators reduce operational costs and improve efficiency.
Scalability & Leveraging AI model
Leveraging ML models on CDR data empowers telecom operators to drive data-driven decisions, boost operational efficiency, and improve customer service while scalable anomaly detection algorithms are essential for handling vast volumes of network-generated data.
Real-Time Detection
Real-time anomaly detection in telecom networks is crucial for promptly addressing network events and security threats, ensuring swift responses to potential disruptions and maintaining the integrity of operations and data.
