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AI use case · Fraud & security

Revenue Assurance — Leakage Detection

Revenue leakage across the mediation-rating-billing chain — from unmediated CDRs, misconfigured product rates and incorrect discounts — accumulates over time and is difficult to attribute to specific root causes.

Anomaly DetectionReconciliation ML
HighBusiness priority
Fraud & securityDomain
5Main data sources

The problem

Revenue leakage across the mediation-rating-billing chain — from unmediated CDRs, misconfigured product rates and incorrect discounts — accumulates over time and is difficult to attribute to specific root causes.

The AI approach

End-to-end reconciliation ML models continuously compare event counts, revenue totals and subscriber billing records across all pipeline stages. Discrepancies above configurable thresholds trigger automated investigation tickets.

How it works

  1. 1
    Reconcile

    Compare network usage, rated usage, billed amounts and payments.

  2. 2
    Detect

    Find gaps and unusual patterns along the chain.

  3. 3
    Size

    Estimate the money at risk for each issue.

  4. 4
    Fix

    Assign owners and track recovery.

Data it uses

MediationRating EngineBilling SystemProduct CatalogOrder Management

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.