A fraud-detection methodology, built before the system that runs it.

The problem

Call-center fraud costs the industry billions every year, and most detection still relies on keyword blacklists that miss anything not said in exactly the expected words. Fraud conducted in Urdu, Arabic, or Spanish is almost completely undetected by English-first tooling, and fraud scripts evolve faster than static rule lists can track.

The concept

CC Fraud Detection is designed around a detailed intelligence framework rather than a keyword list: an 8-layer real-time scoring pipeline, a 43-signal registry spanning metadata, acoustic, NLP, and behavioral categories, and instant-critical override flags for high-severity moments like a gift-card instruction or an OTP-extraction attempt. Fifteen documented fraud typologies — each with verbatim, multi-phase scripts including objection-handling — anchor the design in real observed fraud patterns rather than abstractions.

How the framework is designed to work

• 8-layer real-time detection — planned as a pipeline from session/ingestion signals through to entity-level campaign clustering.
• 43-signal scoring registry — planned as the scoring backbone, with instant-critical overrides for the highest-severity signals.
• Multilingual phrase libraries — already written for English, Spanish, Hindi/Urdu, Mandarin, and Arabic, ready to seed detection once a system is built to use them.
• AI voice-clone detection — planned as a methodology combining GAN-artifact detection, vocal biometrics, and liveness checks.
• A six-phase call-progression model (Hook → Qualify → Escalate → Isolate → Extract → Re-victimize) mapped against the 15 documented typologies.

Upcoming features

The framework above is intelligence and methodology, fully documented today. Planned as the system that would run it:

• A trained detection model and labeled dataset — none exist yet; the framework is a design specification, not a running classifier.
• Live telephony/SIP ingestion and speech-to-text integration.
• An analyst dashboard and a persistent case-management store.
• The "SENTINEL" AI persona referenced in the concept, as an actual prompt/agent implementation.

Who it's for

• Contact centers and BPOs needing multilingual fraud coverage beyond keyword blacklists.
• Banks and telecom operators evaluating a signal-registry approach to fraud scoring.

Platform and stage

Planned deployment: cloud or hybrid, integrated into existing call-center telephony. Stage: concept — the detection methodology, signal registry, and multilingual fraud scripts are fully documented and represent real domain expertise; no detection software has been built yet.