Goal:
Enable telecom operators to prevent revenue leakage from sophisticated fraud schemes by deploying AI systems that learn evolving fraud behavior — reducing dependency on static rule-based detection.
Impact Keywords:
- Revenue Protection
- Adaptive Anomaly Detection
- Fraud Intelligence
- Automation & Model Governance
Approach:
LunarTech Lab designed an advanced anomaly-detection platform that leverages deep learning and graph analytics to uncover patterns invisible to conventional systems.
- Unified Data Fusion Layer:
- Integrated heterogeneous datasets — call detail records, transaction logs, usage patterns, and device fingerprints — into a unified feature space enabling temporal and behavioral correlation.
- Unsupervised Anomaly Models:
- Deployed hybrid unsupervised learning (autoencoders + graph clustering) to detect new fraud vectors without labeled data. Models continuously retrain as network usage patterns shift.
- Real-Time Scoring Engine:
- Built an inference API that classifies transactions in milliseconds, with precision thresholds tunable per region or product line.
- Human-in-the-Loop Feedback:
- Analysts can review flagged cases, providing corrective feedback that fine-tunes model sensitivity — creating a virtuous learning cycle.
- Model Governance & Compliance:
- Logging and monitoring modules ensure interpretability, auditability, and compliance with telecom data regulations.
Summary:
This system transforms fraud detection from static rule sets to self-learning intelligence. By combining behavioral modeling, automation, and explainable AI, LunarTech Lab empowers telecoms to protect revenue in real time and stay ahead of fraud innovation.
