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Kyivstar’s Bogdan Yatsenko explains why a valuable marketing tool like zero-rated data can also create an under-recognized business assurance risk.
Bogdan Yatsenko
Zero-rated data: an emerging blind spot for revenue assurance
Zero-rated data has become a standard commercial tool in modern mobile networks. Operators may opt not to charge for certain types of data to simplify customer journeys, support account top-up, promote selected over-the-top (OTT) services and improve engagement with digital channels. In many cases, zero-rated data delivers clear value: better accessibility, stronger customer experience and increased interaction with services that operators want to encourage.
But in increasingly data-centric, encrypted 4G and 5G environments, zero-rating can also create an under-recognized assurance risk. Traffic intentionally excluded from charging may, under certain conditions, be used in ways that circumvent normal monetization logic. What appears to be legitimate usage in isolation can become a source of hidden revenue leakage when viewed behaviorally and across domains.
For business assurance teams, this is no longer just a tariff-design or network-policy issue. It is becoming a control challenge at the intersection of charging, traffic classification and subscriber behavior.
For many years, telecom fraud and revenue leakage were dominated by voice and messaging scenarios. Today, the landscape is shifting toward data.
Several factors are driving this change:
The result is a more complex monetization environment. Charging accuracy depends not only on rating rules, but also on correct traffic identification, policy enforcement and the consistency of user behavior. In this context, zero-rated categories – especially account top-up access and selected OTT services – can become structural exposure points if they are not continuously reassessed from an assurance perspective.
This does not necessarily imply abuse on a large scale in every network. But it does mean that operators should no longer treat zero-rated traffic as a purely commercial feature. It should also be evaluated as a potential control gap.
The challenge with this type of leakage is that it rarely presents as a single obvious anomaly. More often, it appears as a combination of behaviors that are individually explainable but collectively inconsistent with normal subscriber usage.
Typical indicators may include:
None of these indicators alone is sufficient to prove misuse. However, when they appear together – particularly over time and across segments – they can point to monetization leakage that would be invisible to traditional event-based controls.
This is especially relevant in environments where subscribers have become highly adaptive in the way they consume digital services. As charging and policy logic become more sophisticated, so do user behaviors that exploit their edge cases.
This kind of exposure is difficult to detect because it usually sits within legitimate service frameworks. The traffic category itself is approved. The customer journey is expected. The service is commercially valid. From the perspective of individual systems, nothing may look overtly wrong.
That is precisely why the issue can remain hidden.
Traditional assurance controls are often designed to detect discrete failures: missing call detail records (CDRs), rating mismatches, mediation breaks or configuration errors. Zero-rated traffic misuse, by contrast, may leave no obvious transaction-level exception. The leakage emerges only when operators correlate multiple dimensions such as charging events, network session behavior, subscriber account status, service entitlements and longitudinal usage patterns.
In other words, this is not primarily a reconciliation problem. It is a behavioral analytics problem.
That distinction matters. As networks become more automated and service structures more dynamic, assurance functions need to move beyond static thresholds and binary rule checks. Detecting emerging leakage increasingly depends on identifying abnormal combinations of otherwise legitimate events.
If left unaddressed, the impact can extend beyond a narrow charging discrepancy.
The most obvious effect is under-monetization of data usage that should, in normal circumstances, contribute to paid bundle consumption or incremental top-up behavior. But the wider consequences may be more significant. Operators may see suppressed uptake of add-on packages, distorted usage patterns that undermine commercial assumptions and increased difficulty forecasting real demand.
In roaming scenarios, the risk can be even more material. Traffic that appears commercially harmless at retail level may still generate wholesale cost exposure if consumption patterns do not align with charging expectations. Over time, this can create a margin issue that is difficult to diagnose through standard reporting alone.
There may also be an operational cost. Sustained or unintended traffic loads within zero-rated categories can affect network resources, policy enforcement performance and service quality. When these effects accumulate gradually, they are easy to overlook, particularly if the organization does not treat zero-rated usage as an assurance domain in its own right.
The first step is conceptual: Zero-rated traffic should be reviewed not only as a marketing or customer experience tool, but also as a monetization control surface.
From there, operators should strengthen cross-domain visibility. Effective detection requires the correlation of charging data, session-level network information, subscriber status and service usage over time.
Behavioral segmentation can help identify subscriber groups whose zero-rated usage materially deviates from peer norms. Monitoring should also focus on persistent usage under low-balance or non-paying conditions, as well as sharp concentration in specific zero-rated categories.
Equally important is governance. These risks do not sit neatly within a single function. Fraud management, revenue assurance, charging, network and commercial teams all hold part of the picture. Without a shared view of the exposure, leakage can remain unowned.
More broadly, this issue reflects an important shift in business assurance itself. As operators’ revenues become increasingly data-driven, assurance can no longer rely solely on verifying whether events were processed correctly. It must also ask whether commercial intent, usage behavior and monetization outcomes remain aligned.
Zero-rated services remain valuable. They improve accessibility, support digital adoption and can strengthen customer engagement. But they also introduce new layers of complexity in charging and control.
In today's encrypted, high-volume mobile environments, revenue leakage does not always arise from obvious failures or clearly fraudulent actions. Sometimes it emerges from legitimate mechanisms used in unintended ways. That is what makes zero-rated traffic an emerging blind spot.
Operators that address this early – through behavioral analytics, cross-domain controls and stronger internal ownership – will be better positioned to protect both revenue integrity and network efficiency.
