This is like a smart security guard listening to phone calls in real time. It doesn’t care about the conversation content; it watches the call’s technical fingerprints (who’s calling from where, what device, how the call behaves) to spot patterns that look like scammers and raises an instant alarm.
Reduces losses from phone-based fraud in contact centers by monitoring call metadata in real time, flagging suspicious calls and behaviors before agents share sensitive information or complete risky transactions.
Tight integration with telecom signaling and call metadata streams, plus fraud pattern libraries specific to voice/call behavior that are hard to replicate without carrier-level visibility.
Classical-ML (Scikit/XGBoost)
Time-Series DB
Medium (Integration logic)
Real-time processing of high-volume call metadata streams with low latency, and maintaining accurate fraud detection models as attacker behavior evolves.
Early Majority
Focus on using telecom-grade call metadata and signaling data (rather than only call audio or agent desktop data) to detect fraud patterns in real time, enabling earlier detection in the call flow and simpler integration with existing UC/contact center infrastructure.