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AI use case · Fraud & security

Real-Time Fraud Detection

Fraudulent activations, SIM swaps, account takeovers and international revenue share fraud (IRSF) cause significant direct revenue loss and subscriber harm — often identified days or weeks after the event.

Real-time ML ScoringGraph AnalyticsAnomaly Detection
HighBusiness priority
Fraud & securityDomain
5Main data sources

The problem

Fraudulent activations, SIM swaps, account takeovers and international revenue share fraud (IRSF) cause significant direct revenue loss and subscriber harm — often identified days or weeks after the event.

The AI approach

Real-time ML scoring on every activation, account access and high-value transaction. Behavioural anomaly detection flags deviations from established subscriber patterns. Graph analysis detects coordinated fraud rings. Automatic suspension triggers for high-risk events.

How it works

  1. 1
    Stream

    Score events as they happen: calls, top-ups, orders, payments.

  2. 2
    Detect

    Combine rules with models for new fraud patterns.

  3. 3
    Act

    Block, challenge or review high-risk events.

  4. 4
    Learn

    Feed confirmed fraud back into the models.

Data it uses

Order ManagementCRMCharging SystemIAM Access LogsNetwork Signalling Data

How to measure value

Practical tips

Standards & references

Data governanceUse governed, consented data only
Responsible AIExplainable, monitored, human-in-the-loop

Related pages

This page describes generic industry practice and public standards. It is not based on, and does not describe, any particular vendor's product or operator's systems.