Telecoms operators built the infrastructure. The question now is whether they can monetise the data that flows across it — while modernising the BSS/OSS stack that was designed for voice and SMS, closing the revenue leakage that telecom fraud creates, and satisfying regulators who are watching AI adoption and data practices with increasing scrutiny.
Telecommunications
The structural tension in telecoms has been clear for a decade: ARPU pressure from OTT substitution, capex intensity from spectrum and network investment, and a subscriber base that has become sophisticated enough to switch operators for marginal price advantages. The 5G investment cycle has intensified this tension — operators have spent significantly on spectrum and infrastructure and are under pressure to demonstrate the revenue model that justifies it.
The answer, for most operators, lies in data — but realising it requires capabilities most have not yet built. Network data monetisation — selling anonymised insights from network telemetry to enterprise customers, using network intelligence for B2B service differentiation, and building vertical IoT services on private 5G — requires a data platform and commercial capability that legacy BSS/OSS stacks were not designed to support.
Meanwhile, telecom fraud remains a structurally significant problem. Interconnect bypass, SIM box fraud, subscription fraud, IRSF (International Revenue Share Fraud), and increasingly AI-generated voice fraud are collectively removing 2–3% of industry revenue annually. The detection approaches used by most operators — rule-based systems with high latency — are increasingly inadequate against fraud operations that adapt faster than rule updates can be deployed.
Customer intelligence has become the other central battleground. Operators with sophisticated churn prediction, personalised offer engines, and NPS-correlated network quality analytics are retaining subscribers at materially better rates than those still using segment-based retention programmes. The data to build this capability sits in CDRs, network telemetry, and CRM systems — the challenge is the analytics architecture and ML capability to use it.
We work with mobile network operators, fixed-line and broadband providers, enterprise telecoms companies, MVNOs, and the technology vendors and system integrators serving the telecom sector.
IRSF, SIM box, interconnect bypass, and subscription fraud operating at a scale and sophistication that outpaces the rule-based fraud management systems that most operators are running.
Legacy billing, rating, and network management stacks that cannot support 5G network slicing, IoT billing complexity, dynamic pricing, or the API-first partner ecosystem that enterprise 5G services require.
Subscriber churn driven by price sensitivity and service experience — without the predictive models that identify at-risk subscribers early enough to intervene effectively, before the decision to switch is made.
Enormous volumes of network telemetry — quality metrics, usage patterns, location data, IoT signals — that sit in OSS systems with no analytical layer capable of translating them into product intelligence or enterprise data products.
Securing network infrastructure — RAN, core, and transmission — against threats that are increasing in both volume and sophistication, including SS7 vulnerabilities, Diameter protocol attacks, and nation-state targeting of critical telecoms infrastructure.
TRAI regulations on data localisation, customer consent for marketing communications, and the intersection of telecom data with DPDPA 2023 obligations for customer personal data processing.
Six capabilities matched to the specific commercial and technical challenges that telecoms operators face — with the domain fluency to engage at the CDR, network, and BSS/OSS level.
ML-based fraud detection models that analyse CDR patterns, signalling anomalies, and subscriber behaviour across the full fraud taxonomy — IRSF, SIM box, subscription fraud, and bypass detection. Significantly lower false positive rates than rule-based systems, with sub-second scoring for real-time fraud blocking.
Risk Assessment →Strategic advisory and programme management for BSS/OSS cloud-native transformation — whether replacing legacy billing with a modern charging platform, implementing a digital BSS layer alongside existing systems, or designing the API gateway that enables partner and enterprise service integration.
Digital Transformation →ML churn propensity models trained on usage patterns, network quality experience, payment behaviour, and service interactions — identifying at-risk subscribers 30–60 days before churn with the precision to make targeted retention intervention economically rational.
AI & ML Advisory →Network intelligence platform design — ingesting RAN telemetry, probes, and CDR streams into a real-time analytics layer that supports QoE monitoring, network planning, and the external data products that enterprise and B2B customers are beginning to pay for.
Data Analytics →Security architecture for telecom-specific threat vectors — SS7 and Diameter protocol security monitoring, RAN security hardening, 5G core security architecture, and signalling firewall assessment — alongside conventional IT security for the enterprise systems that support network operations.
Cybersecurity Consulting →TRAI regulatory compliance programme design — customer consent management for marketing, data localisation architecture for subscriber data, and the intersection of DPDPA 2023 obligations with telecom-specific data processing. Revenue assurance framework design alongside compliance controls.
Compliance & Risk →5G creates new data assets — network telemetry, location intelligence, IoT signals, quality of experience metrics. The operators who monetise it first will hold a structural advantage. These are the three primary monetisation paths we design analytics infrastructure for.
Using network data to improve internal decisions — network planning, customer experience management, churn prediction, and targeted retention. The highest-certainty monetisation path because it drives direct cost and ARPU impact.
Selling aggregated, anonymised network insights to enterprise customers — mobility analytics for retail site selection, footfall measurement for urban planners, supply chain movement intelligence for logistics operators.
Building managed service and analytics offerings on top of private 5G network deployments — manufacturing IoT, smart port operations, healthcare wireless connectivity, and the vertical applications that turn network infrastructure into recurring services revenue.
The telecoms operators who win the next decade will be those who close the gap between the data they generate and the intelligence they act on. Book a conversation with our telecom practice team — we will assess where your biggest data, fraud, and transformation opportunities are.