Roaming Fraud
Roaming and wholesale fraud can generate significant losses within hours, often before delayed data and manual reviews reveal the activity. The CFCA Global Fraud Loss Survey 2025 cites $3.82 billion in wholesale fraud-arbitrage losses in its six-year comparison, while earlier surveys reported $2.33 billion in wholesale fraud losses.
Overview
Roaming fraud can include SIM cloning, unauthorized usage, and rapid spend escalation while a subscriber is roaming. Subex helps CSPs detect unusual roaming behavior early using roaming feeds and behavioral signals, enabling targeted interventions before exposure grows.
The Challenge
Roaming and wholesale-related exposures often show up as arbitrage and wholesale fraud–arbitrage is shown at $3.82B in the six-year comparison, and wholesale fraud has been reported at $2.33B in prior surveys. (Source: CFCA Global Fraud Loss Survey 2025).
Roaming usage can ramp quickly, creating high-loss events within hours.
Delayed roaming data and manual review increase fraud runtime.
Fraud patterns vary by destination, device, and subscriber profile.
Customer disputes and bill shocks damage trust and increase churn.
Real Impact. Real Efficiency. Real Results.
Detect and respond earlier to reduce roaming loss exposure.
Protect customer experience by preventing bill shocks and disputes.
Improve operational efficiency with prioritized cases and clear evidence.
Support compliance and partner management by improving control and reporting.
Dynamic spend monitoring limits high-value single-event losses.
Identify high-risk destinations and roaming partners proactively.
Detect anomalies within hours rather than days.
Where Channels Meet Control
Detect misuse. Flag spikes. Prevent fraud loss.
Ingestion and analysis of roaming feeds (e.g., NRTRDE/TAP) where available.
Behavioral analytics to identify rapid spend escalation, unusual destinations, and abnormal usage mix.
Policy-driven interventions: usage caps, alerts, barring, or step-up verification.
Investigation workspace to validate evidence across roaming and core usage data.
- Case Study
Batelco Nips Roaming Fraud in the Bud with Subex’s Fraud Management System
- Whitepaper
AI Agents in Telecom Fraud Management
- E-book
AI-First Defense Against Telecom Fraud
Resource Center
- Case Study
The Batelco-Subex Partnership in Fighting Telecom Fraud
Case Study The Batelco-Subex Partnership In Fighting Telecom Fraud What’s...
- Whitepaper
Be Vigilant Against SMS Fraud with Proactive Mitigation
- Point of View
Addressing SIM Box Fraud with Machine Learning
Frequently Asked Questions
What are the most common types of roaming fraud?
Common roaming fraud scenarios include:
- International Revenue Share Fraud (IRSF)
- Wangiri / One-ring callback scams
- SIM box abuse while roaming
- Stolen SIM and handset roaming misuse
- Fraudulent roaming activation and subscription fraud
Why is roaming fraud difficult to detect early?
Roaming fraud is challenging due to:
- Latency in TAP file delivery
- Limited real-time visibility of roaming events
- Multiple partner networks and jurisdictions
- Rapid fraud execution within hours
How quickly can roaming fraud losses escalate?
Roaming fraud is often executed in a short window – sometimes within minutes to hours, leading to substantial losses before traditional billing systems detect the issue.
Can the platform integrate with existing roaming feeds and systems?
Yes. The solution supports ingestion and analysis of roaming feeds such as NRTRDE and TAP where available. It integrates with existing Fraud Management Systems, CRM platforms, and policy control systems to enable automated or analyst-driven intervention.
How does the platform utilize NRTRDE and TAP files for fraud detection?
The solution ingests NRTRDE (Near Real-Time Roaming Data Exchange) feeds where available for early anomaly detection, and correlates them with TAP files for post-event validation and settlement analytics. This layered approach helps reduce detection latency while maintaining reconciliation accuracy.
How does the solution differentiate legitimate high-usage roamers from fraud cases?
Behavioral models compare roaming activity against subscriber baseline patterns, historical travel history, device profile, usage mix, and customer segment. This reduces false positives while still flagging abnormal spend acceleration or destination anomalies.