Think of it as a 24/7 security guard that watches every phone call, text, and transaction in real time and raises a flag when something looks like fraud, even if no human has seen that pattern before.
Reduces financial losses and customer churn from telecom fraud (e.g., subscription fraud, SIM‑swap, international revenue share fraud) by detecting suspicious behavior earlier and more accurately than rule-based systems alone.
If implemented well, the moat comes from proprietary network usage data, labeled fraud events, and the in-house expertise to tune models and rules to local fraud patterns and regulations—not from the algorithms themselves, which are widely available.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Real-time scoring at telecom scale (high events-per-second) and maintaining data quality/feature freshness across distributed systems.
Early Majority
Compared with generic fraud analytics, telecom-focused deployments must handle very high event volumes, real-time constraints, and domain-specific fraud typologies (e.g., roaming abuse, SIM box), making telco-specific features and data pipelines the main differentiator.