Should you build or buy Telecom Revenue Assurance & Fraud Management (RAFM)?
Telecom Revenue Assurance & Fraud Management (RAFM) software closes the gap between what a carrier's network generates and what it bills and collects, and detects fraudulent usage such as SIM-box bypass, Wangiri, IRSF, roaming and interconnect abuse before it erodes margin. It works on CDR streams and mediation data and has two halves: a detection layer (rules, anomaly detection, behavioral profiling) and an assurance layer (revenue reconciliation, controls libraries, roaming and interconnect settlement, NRTRDE and TAP workflows, case management, auditability).
Copy reviewed 2026-09-22 · Research revision 2026-09-14
Sources: jio.com; airtel.in; pyod.readthedocs.io; riverml.xyz; subex.com; mobileum.com
Split the two halves before deciding. Fraud detection on your own telemetry is now buildable and several of the largest carriers have built it. Revenue reconciliation, settlement, controls and case management are not being self-built anywhere, and no open-source project covers them. The realistic question is where the boundary between your models and the vendor's platform should sit, not whether to replace the platform.
Build it, buy it, or bridge?
When building makes sense
Building applies to the detection layer, and only to it. ML on CDR streams (anomaly detection, time-series pattern matching, behavioural profiling, bypass identification) is mature, the fraud patterns are shared industry knowledge, and the largest carriers have shipped production systems on their own telemetry: Reliance Jio's FraudX processes roughly 60 billion events a day across SIM-box, Wangiri, IRSF, bypass, recharge, roaming and dealer fraud, and Bharti Airtel's data scientists built a real-time system that flags up to 100 million spam calls a day. The case is strongest where detection must be tuned to your network topology, traffic mix and dealer channel, where you want to move faster than a vendor release cycle on new fraud types, and where the event volume justifies a standing data science team. What no carrier builds is the assurance half: revenue reconciliation, controls libraries, roaming and interconnect settlement, NRTRDE and TAP workflows, and case management. Open-source maturity for a production RAFM application is low, and there is no equivalent to a vendor's fraud typology library. A team that sets out to replace the whole suite is signing up for that half too.
When buying makes sense
Buying the platform is the default for the assurance half and for any carrier without a data science team. Subex (ROC, HyperSense), TEOCO, Mobileum (formerly WeDo) and Mavenir carry maintained typology libraries, settlement and reconciliation workflows, and integrations with legacy mediation that a self-build would spend years reproducing. Fraud detection protects margin but does not win customers; no carrier gains share because its detection rate is 20 percent better. That keeps this a cost decision: compare the licence against what your team would spend to build and operate the detection layer, and remember that the platform stays in place for everything around it either way. The operators who built their own detection (Jio, Airtel, Vodafone) kept packaged RAFM for reconciliation, settlement and case management, and that boundary is the one to copy.
The desk read
Fraud detection on CDR streams is now a data engineering problem the largest carriers have solved for themselves: Reliance Jio's FraudX runs rules with Isolation Forest, XGBoost and behavioural profiling over about 60 billion events a day, Bharti Airtel's own data scientists built its real-time spam and fraud system, and open-source libraries such as PyOD and River cover the algorithms. The fraud patterns (SIM-box, Wangiri, IRSF, roaming and interconnect bypass) are shared industry knowledge; what a carrier tunes is thresholds, traffic signatures and dealer behaviour on top of them.
The other half of the suite is not moving. No operator self-builds revenue reconciliation, controls libraries, roaming and interconnect settlement, NRTRDE and TAP workflows or case management, and open-source maturity for a production RAFM application is low, with nothing equivalent to a vendor's fraud typology library. Fraud protection is margin hygiene, not a competitive weapon, so the question is where the boundary between your models and the vendor's platform sits. Jio, Airtel and Vodafone all settled on the same answer: proprietary detection on top of a licensed platform.
Frequently asked
What is Telecom Revenue Assurance & Fraud Management (RAFM) software?
Software that closes the gap between what a carrier's network generates and what it bills and collects, and detects fraudulent usage such as SIM-box bypass, Wangiri, IRSF, roaming and interconnect abuse. It runs on CDR streams and mediation data and has a detection layer and an assurance layer (reconciliation, controls, settlement, case management).
When does building Telecom RAFM software make sense?
For the detection layer, at carriers with a data science team and the event volume to justify it. ML on CDR streams is mature and Jio, Airtel and Vodafone have shipped production fraud detection on their own telemetry. Nobody builds the assurance half; those carriers kept a packaged platform for reconciliation, settlement and case management.
When does buying Telecom RAFM software make sense?
For the assurance half always, and for detection at any carrier without ML depth or where legacy mediation integration is the hard part. Specialised vendors carry maintained typology libraries and settlement workflows that a self-build would take years to reproduce, for a capability that protects margin rather than winning customers.
What are the main Telecom RAFM vendors?
Representative vendors include Subex (ROC/HyperSense), Araxxe, Subex (ROC, HyperSense), TEOCO, Mobileum (formerly WeDo Technologies), Mavenir Fraud & Security Suite. B4 Pro includes the category score and the full vendor list.
How is AI changing the RAFM market?
It has made the detection layer buildable: the algorithms (isolation forests, gradient boosting, behavioural profiling) are in open-source libraries and the largest carriers run proprietary models on their own telemetry, with VodafoneThree reporting a 44 percent lift in detection after adopting AI. It has not touched the assurance layer. Vendors such as Subex HyperSense are AI-native for detection and remain the only production option for reconciliation, settlement and case management, so the market is settling on a boundary rather than a replacement.