AI USE CASE
International Revenue Share Fraud Detection
Detect and block artificially inflated premium-rate traffic before it drains revenue.
What it is
Machine learning models analyse call detail records in near real-time to identify abnormal traffic spikes to international premium-rate numbers — a hallmark of International Revenue Share Fraud (IRSF). By flagging suspicious routes within minutes rather than days, telecom operators can cut fraud losses by 30–60% and reduce manual investigation workload by over 50%. Early detection also protects interconnect partners and avoids costly settlement disputes with wholesale carriers.
Data you need
Historical and real-time call detail records (CDRs) including originating/terminating numbers, timestamps, duration, and routing metadata over at least 6–12 months.
Required systems
- erp
- data warehouse
Why it works
- Establish a near-real-time CDR streaming pipeline so models score traffic with sub-minute latency.
- Implement a feedback loop where analyst verdicts on alerts continuously retrain the model.
- Define clear escalation and automatic blocking thresholds agreed between Revenue Assurance and Network Operations.
- Regularly benchmark model performance against known IRSF typologies published by the GSMA Fraud Intelligence team.
How this goes wrong
- High false-positive rates cause legitimate traffic to be blocked, damaging wholesale partner relationships.
- Fraudsters adapt patterns faster than the model is retrained, allowing new attack vectors to go undetected.
- Incomplete or delayed CDR ingestion creates blind spots in near-real-time detection pipelines.
- Siloed ownership between Revenue Assurance and IT delays alert response times, negating detection speed.
When NOT to do this
Do not deploy this solution if your CDR pipeline latency exceeds 15–30 minutes, as near-real-time detection becomes impossible and fraud losses will have already materialised before any alert is raised.
Vendors to consider
Sources
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