Large and fast-moving mobile operators may struggle to ensure constantly changing networks are protected. Revenue Assurance systems may typically only analyze small data samples to confirm and assure billing and revenue accuracy.
Machine Learning Approach
Mavenir’s Revenue Assurance module utilizes a Machine Learning-based system of reference to identify anomalous behavior such as risky package plans, abusive interconnect agreements or network changes.
Data Types include CDR’s, XDR’s, customer data, retail /interconnect rate figures, etc.
- Hundreds of SIMs roaming on a low- cost package plan being used to terminate international calls on a competitor network in the same country – similar to a GSM Gateway
- ‘Test’ SIMs providing free usage were identified as being reused globally, but absent from the billing system.
- Arbitrage: Hundreds of roaming SIMs that called non-geographic numbers with a termination cost 10x times higher than the rate charged to the customer, to take advantage of arbitrage to inflate revenue to their own NGS’s.
- A change on the HLR unknowingly provided free GPRS data usage to thousands of SIMs
- CDR Reconciliation identified large amounts of TD35 files, absent from local billing systems