Customers who leave before your retention team knows they are at risk, network faults found after subscribers call to complain, and fraud patterns that rules catch only after the damage is done: these are the operational and revenue problems that AI addresses in telecom.
We build AI systems for telecom operators, MVNOs, and ISPs: churn prediction, network anomaly detection, AI-powered customer support for billing and service queries, intelligent fraud detection for usage anomalies and SIM swap fraud, predictive maintenance for network assets, and demand forecasting for network capacity planning. Every system is scoped against your subscriber data, network telemetry, and a specific retention or operational outcome.
Churn prediction models that score each subscriber by departure risk 30-60 days before they cancel
Network anomaly detection that surfaces fault signatures in telemetry data before subscribers report service issues
SIM swap and usage anomaly fraud detection that flags suspicious patterns before significant revenue impact
Capacity demand forecasts that let network planning teams allocate investment ahead of congestion
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Are you finding out a subscriber is about to churn only after they've already submitted a PAC code or called to cancel?
Is your network operations centre responding to faults that your telemetry data could have predicted 24-48 hours earlier?
In short
RaftLabs builds AI systems for telecom operators, MVNOs, and ISPs that shift operations from reactive to anticipatory. Subscriber churn prediction models score each customer at a 30-60 day horizon so retention workflows trigger before cancellation requests arrive. Network anomaly detection learns the expected behaviour of each network element and surfaces early degradation signatures before subscribers report service issues. SIM swap fraud detection scores each swap request using account contact history, device change patterns, and timing signals to flag fraud before the swap completes. Engagements are scoped at a fixed price after a discovery phase that maps your subscriber data and network telemetry to the specific AI capability being built.
Trusted by
Telecom AI is most valuable when it shifts operations from reactive to anticipatory. The churn signal is in the usage data before the subscriber calls. The network fault signature is in the telemetry before the service degrades. The fraud pattern is in the account activity before the swap completes. The question is whether a model is reading those signals.
Capabilities
Classification models trained on your subscriber records, usage history, service contact logs, and network quality experience data. Features include: contract tenure, usage volume trend (voice minutes, data GB over last 3 months vs. 6 months prior), bill shock events (charges significantly above average), number of contacts to support in last 90 days, complaints history, network quality score (derived from drive test data or probe data for the cell sites the subscriber uses), and days-to-contract-end. Gradient boosting (XGBoost or LightGBM) produces probability estimates calibrated with Platt scaling so the output 0.72 churn probability represents approximately 72% actual churn rate in that score band, making offer cost decisions tractable. SHAP values per subscriber explain the top contributing factors to each risk score.
Scores each subscriber by churn probability at a 30-60-90 day horizon with separate models per horizon. High-risk subscribers enter a retention workflow, a targeted offer, proactive service call, or tariff review, before they submit a cancellation or PAC request. Intervention threshold is tuned against subscriber value tiers (ARPU, CLV) and offer cost structure to avoid discounting subscribers who would have stayed without an incentive. Uplift modelling (the fraction of at-risk subscribers who retain because of the intervention, not despite it) measures actual ROI of retention spend and informs future campaign budget allocation.
Models trained on your network telemetry, KPIs per cell site (RSRP, SINR, CQI, PRB utilisation, handover success rate, call drop rate), backhaul link performance (utilisation, latency, packet loss), and core node metrics, that learn the expected operating pattern for each network element using a rolling 28-day hourly baseline with time-of-day and day-of-week normalisation. Flags deviations from expected values that surface early degradation before service impact is subscriber-visible. Anomaly scoring uses Z-score on each KPI (Z > 2.5 = alert), with multi-KPI correlation applied to reduce single-metric false positives: a cell site anomaly must show deviation in at least 2 correlated KPIs before escalating to an alert. Prioritised alert queue for the network operations centre ranked by anomaly severity and estimated affected subscriber count. Reduces mean time to detect (MTTD) and mean time to resolve (MTTR) network faults compared to threshold-only alerting because the signal appears earlier in the degradation curve.
Conversational AI for billing queries, service status checks, usage explanation, and account management. Trained on your product catalogue, billing rules, and historical support transcripts. Resolves routine contacts, balance checks, roaming query, bill explanation, without agent involvement. Passes complex complaints and technical faults to human agents with full context and contact history. Integrates with your CRM and BSS via API. Reduces cost-per-contact on high-volume routine query types without reducing resolution quality.
SIM swap fraud detection that scores each swap or port-out request by fraud probability using account activity history, recent contact patterns, and request timing. Usage anomaly detection that flags abnormal call or data volumes by subscriber, consistent with IRSF, wangiri, or roaming fraud patterns, before significant revenue exposure accumulates. Both models are trained on your historical labelled fraud data and tuned to your fraud pattern mix. Outputs a prioritised review queue for your fraud team rather than a binary block decision.
Models trained on sensor and monitoring data from your network hardware, power systems, cooling units, radio units, and transmission equipment, that surface failure risk before outage. Inputs include temperature, power draw, error rates, and hardware health signals. Gives field engineering teams a prioritised maintenance list based on actual failure probability, not calendar intervals. Reduces reactive maintenance costs and reduces the number of unplanned outages caused by equipment failure.
Traffic demand forecasts at the cell sector, backhaul segment, and core node level over planning horizons of weeks to months. Trained on historical traffic data, subscriber growth trends, and event calendars. Uncertainty-bounded forecasts feed capacity upgrade scheduling decisions. Helps network planning teams invest in the right locations before congestion affects subscriber experience rather than after. For operators planning 5G rollout, demand forecasting supports geographic prioritisation of capacity investment.
Churn, fraud, network faults, or support costs: tell us the specific problem and we will assess which AI system reduces it and what your data supports.
AI for Telecom industry
industry context and AI use cases across telecom.
Predictive Analytics
churn, demand, and anomaly forecasting models.
AI-powered Customer Support
AI support automation across industries.
AI Development
end-to-end custom AI system builds.
Machine Learning Development
ML model development and deployment.
Telecom Software Development, telecom industry software context and operational AI use cases
AI-powered Customer Support Automation, AI support automation across industries
Machine Learning Development, ML model development and deployment
Churn prediction for telecom operators is a binary classification problem: for each subscriber, predict the probability that they will leave within a defined horizon, typically 30, 60, or 90 days. The model uses features derived from your subscriber records and usage data: contract remaining term, tariff type, usage trend over the last 3 months (is data consumption falling?), customer service contact history (how many billing complaints in the last 60 days?), payment history (late payments), handset age, and network quality experience on the cell sites the subscriber uses most. Subscribers above a churn probability threshold enter a retention workflow: a targeted offer, a proactive service call, or an upgrade prompt, calibrated to the subscriber's value tier and predicted departure reason. The intervention is applied before the subscriber has decided to leave, not after they call to cancel. A key design decision is the prediction horizon and the intervention threshold. Too broad an intervention and you discount subscribers who would have stayed without an offer. We tune the threshold against your subscriber value distribution and retention offer cost structure.
Standard network monitoring generates alerts when a KPI crosses a threshold: if packet loss on a link exceeds 5%, raise an alert. Threshold alerting catches obvious degradation but misses subtle early degradation signatures and generates large volumes of false positives when thresholds are set broadly. Network anomaly detection models learn the expected behaviour of each network element, cell site, backhaul link, core node, under different traffic conditions, time of day, and seasonal patterns. A deviation from the model's expected value for the current conditions surfaces as an anomaly, even if the absolute value of the KPI hasn't crossed a static threshold. This means you see the early signature of a failing piece of equipment, a gradual increase in error rate on a specific link, a subtle rise in latency on a cell site, days before it becomes a service-affecting fault. Output is a prioritised alert queue for the network operations centre, ranked by anomaly severity and estimated subscriber impact. Reduces mean time to detect faults and reduces the volume of subscriber-reported service issues that arrive before the NOC has acted.
SIM swap fraud detection is a classification model that scores each SIM swap request or port-out request by the probability that it is fraudulent. Features include the account holder's history of contact with customer service in the preceding 48-72 hours (fraudsters typically call to update contact details before initiating a SIM swap), device change history, account age, recent address or email changes, time of request, and the channel through which the request was submitted. High-risk swap requests are held for enhanced verification, a call-back to the account holder's registered number, a branch visit, or additional identity documents, rather than being processed automatically. The model is trained on your historical SIM swap data labelled with confirmed fraud outcomes. Because SIM swap is used to take over high-value accounts and bypass SMS-based two-factor authentication, early detection prevents downstream fraud losses that are disproportionate to the cost of the swap itself.
Network capacity demand forecasting predicts traffic load at the cell site, backhaul segment, or core node level over planning horizons of weeks to months. Inputs include historical traffic data by element and time period, subscriber growth projections, planned network events (major sporting events, festivals), and new site activation schedules. The model produces forecasts at the granularity your planning team uses, daily traffic peaks by cell sector, weekly throughput by backhaul segment, with uncertainty bounds. Network planning teams use these forecasts to schedule capacity upgrades before congestion occurs rather than reacting to subscriber complaints. For operators rolling out 5G, demand forecasting also helps prioritise which geographic areas get capacity investment first based on projected traffic demand rather than coverage maps alone.
Work with us
We scope AI for Telecom Operators in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.