A tier-1 telecom operator serving over 20 million subscribers, including a large base of international roaming customers, was experiencing significant revenue leakage due to roaming fraud. With global traffic volumes rising and fraud tactics becoming more sophisticated, traditional fraud monitoring systems were no longer sufficient.
Delayed detection meant fraudulent activity often went unnoticed until days or weeks later—by which time substantial charges had already accumulated.
The Challenge: Roaming Fraud Draining Revenue and Trust
Roaming fraud posed both financial and reputational risks for the operator. As international usage increased, manual and rule-based fraud controls failed to scale.
Key Challenges
1. SIM Box Fraud
International calls were illegally rerouted through low-cost channels
Legitimate roaming revenues were bypassed
2. Subscription Fraud
Fake or stolen identities were used to activate SIMs
Fraudsters exploited roaming services before accounts could be verified
3. Delayed Fraud Detection
Fraudulent transactions were detected days or weeks after occurrence
Limited ability to recover lost revenue
4. High Financial Losses
Annual losses ran into millions of dollars
Direct impact on profitability
5. Customer Dissatisfaction
Genuine customers faced:
Unexpected roaming charges
Account suspensions due to delayed fraud actions
Manual fraud monitoring was no longer viable given the scale of international traffic and the speed at which fraud occurred.
The Solution: AI-Powered Real-Time Fraud Detection by Amantra
To combat roaming fraud proactively, Amantra deployed an AI-driven, real-time fraud detection platform designed to identify and stop fraudulent activity before revenue leakage occurred.
The platform combined streaming analytics, behavioral AI models, and autonomous decisioning to protect roaming revenues at scale.
Key Solution Capabilities
Real-Time Streaming Data Analysis
Continuous monitoring of:
Call Detail Records (CDRs)
Usage logs
Roaming activity
Analysis performed in milliseconds to enable instant response
Behavioral AI Models
Machine learning models identified abnormal patterns such as:
Sudden location changes
Unusual international calling volumes
Rapid usage spikes inconsistent with customer history
Adaptive Risk Scoring
Every transaction was assigned a dynamic fraud risk score
Scores updated in real time based on evolving behavior
Instant Blocking and Alerting
High-risk activity triggered:
Automatic service suspension
Real-time alerts to fraud operations teams
Prevented fraud escalation without human delay
Continuous Learning and Model Optimization
AI models learned from:
Confirmed fraud cases
False positives
New fraud patterns
Detection accuracy improved continuously over time
The Results: Fraud Prevention at Speed and Scale
The AI-driven fraud detection system delivered rapid and measurable impact:
80% Reduction in Roaming Fraud Incidents
Fraud attempts were stopped before escalating.Millions in Prevented Revenue Leakage
Real-time blocking eliminated delayed loss recovery.95% Fraud Detection Accuracy
Adaptive learning improved precision and reliability.40% Reduction in False Positives
Genuine customers experienced fewer disruptions.Stronger Customer Trust
Proactive fraud protection improved customer confidence in roaming services.
Bottom Line: From Delayed Detection to Real-Time Fraud Prevention
By shifting from post-event fraud analysis to real-time, AI-driven prevention, the telecom operator transformed its fraud management strategy. Roaming services became more secure, revenues were protected, and customer experience improved—without increasing operational complexity.
AI-driven fraud detection enabled the operator to stay ahead of evolving fraud tactics while delivering safe, seamless, and trusted roaming services worldwide.