Imagine a super-watchdog sitting on your telecom network lines, constantly watching billions of calls, texts, and data sessions in real time. It has seen thousands of fraud tricks before and can spot new scams the moment patterns start to look suspicious, then automatically shut them down before they spread.
Telecom operators face rising losses and reputation damage from sophisticated fraud (e.g., SIM swap, account takeover, subscription fraud, international revenue share fraud). Manual rules and after-the-fact audits can’t keep up with evolving attack patterns; the AI system continuously monitors traffic and customer behavior to detect and block fraud earlier and more accurately.
Deep, telco-specific behavioral and network data combined with historical fraud cases and domain expertise; integration into core telco provisioning, billing, and customer-care workflows increases stickiness and makes models hard for competitors to replicate quickly.
Hybrid
Feature Store
High (Custom Models/Infra)
Real-time scoring at telco scale (high-throughput, low-latency inference and feature computation across billions of events per day), plus data privacy and regulatory constraints on customer traffic analysis.
Early Majority
Positioned around telco-grade scale, domain-specific fraud typologies, and close integration with core network and billing systems, rather than generic financial fraud detection; leverages proprietary subscriber and traffic data to tune models to telecom-specific schemes.