About Client
The client is a leader in IP networking technology with a strong track record in developing and deploying next-generation carrier-grade Session Border Controllers (SBCs). The company focuses on enabling telecom operators to transition toward modern all-IP communication infrastructures.
Its SBC platforms are widely deployed by leading telecom operators across the globe to manage and secure voice traffic. As telecom networks handle massive volumes of calls and transactions every second, the need for built-in real-time fraud detection for telcos has become increasingly critical.
Background
Telecom network operators—who are the client’s primary customers—were experiencing significant financial losses due to fraudulent activities within voice traffic and network usage.
Existing systems lacked the infrastructure necessary to run advanced analytics and clustering algorithms capable of identifying suspicious behavior in real time. As telecom networks grew in scale, detecting fraud required processing massive volumes of call detail records (CDRs) and network data streams.
The client needed a scalable analytics framework capable of supporting advanced fraud pattern recognition while operating within the resource constraints of an SBC environment. Without such capabilities, telecom operators struggled to proactively detect fraudulent calls, SIM misuse, or abnormal traffic patterns.
Objective
The objective was to design a system capable of storing and analyzing large volumes of call data directly on a single SBC box while remaining scalable enough to retain more than a year of historical data.
The system also needed to analyze this data to generate meaningful fraud patterns and identify suspicious activity. To support operational teams, the platform had to deliver real-time fraud detection for telcos through dashboards and monitoring reports. Key technical challenges included:
- Massive Data Volume and Velocity
Telecom networks generated more than 10,000 calls per second, requiring high-throughput data processing. - Complex Application Data Formats
Call records were stored in diverse formats including flat files, Google protocol buffers, and nested data structures. - Limited Infrastructure Resources
The solution needed to run within the hardware limitations of the SBC box while supporting scalability through additional nodes.
