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AI use case · Fraud & security

SIM Swap Fraud Detection

Fraudulent SIM swap requests — impersonating legitimate subscribers to redirect their MSISDN to a new SIM — enable account takeover and financial fraud.

Binary ClassificationRisk Scoring
HighBusiness priority
Fraud & securityDomain
5Main data sources

The problem

Fraudulent SIM swap requests — impersonating legitimate subscribers to redirect their MSISDN to a new SIM — enable account takeover and financial fraud. Detection relies on manual verification processes that fraudsters circumvent.

The AI approach

ML models score each SIM swap request against subscriber history, behavioural patterns and request characteristics. High-risk requests automatically routed for enhanced verification or blocked pending confirmation.

How it works

  1. 1
    Check

    Score every SIM swap and port-out request.

  2. 2
    Signal

    Use device, location, channel and recent account changes.

  3. 3
    Challenge

    Ask for stronger checks on risky requests.

  4. 4
    Share

    Offer a SIM-swap check to banks and partners.

Data it uses

CRMParty ManagementIAMHistorical SIM Swap DataDevice History

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.