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CDR Rejection Trend Analysis & Classification

CDRs rejected at different mediation and rating stages remain unrated, causing direct revenue leakage.

Classification MLPattern Analysis
MediumBusiness priority
Revenue & billingDomain
4Main data sources

The problem

CDRs rejected at different mediation and rating stages remain unrated, causing direct revenue leakage. Root causes span file format errors, switch misconfigurations and rating rule gaps — difficult to diagnose at scale.

The AI approach

Classify rejected CDRs by error type using ML classification models. Identify recurring patterns and correlate with upstream source systems. Automatically suggest reprocessing actions per rejection category.

How it works

  1. 1
    Collect

    Gather rejected usage records with their error codes from mediation.

  2. 2
    Classify

    Group rejections by cause, source network element and record type.

  3. 3
    Trend

    Learn normal rejection levels per source and spot unusual rises.

  4. 4
    Alert & fix

    Route each cluster to the owning team with a suggested fix.

Data it uses

Mediation PlatformRating EngineCDR Dump (all service types)Error Log Store

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.