AI for Telecom Operators

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 volume trend over the last 3 months, customer service contact history, payment history, handset age, and network quality experience on the cell sites the subscriber uses most. Subscribers above a churn probability threshold enter a retention workflow before they submit a cancellation or PAC request. The intervention threshold is tuned against subscriber value tiers and offer cost structure to avoid discounting subscribers who would have stayed without an incentive.

Standard network monitoring generates alerts when a KPI crosses a threshold. Threshold alerting catches obvious degradation but misses subtle early 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 under different traffic conditions, time of day, and seasonal patterns. A deviation from the model's expected value surfaces as an anomaly even if the absolute KPI hasn't crossed a static threshold. This means you see the early signature of a failing piece of equipment 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.

SIM swap fraud detection is a classification model that scores each SIM swap 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, device change history, account age, recent address or email changes, time of request, and the submission channel. High-risk swap requests are held for additional verification 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 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 such as major sporting events, and new site activation schedules. The model produces forecasts at the granularity your planning team uses, with uncertainty bounds. Network planning teams use these forecasts to schedule capacity upgrades before congestion occurs rather than reacting to subscriber complaints.

Telecom AI projects at RaftLabs are scoped and fixed-price before development starts. A focused system such as a churn prediction model or SIM swap fraud classifier typically runs 10-16 weeks from kick-off to production. Cost depends on the scope of data integration, the number of models, and the complexity of the intervention workflow. You receive a written scope and fixed price after a discovery phase, with no development starting before sign-off.

For churn prediction, we need subscriber records, usage history (voice, data, SMS by month), customer service contact logs, and ideally network quality data per subscriber. For fraud detection, we need historical SIM swap or port-out records labelled with confirmed fraud outcomes, account activity logs, and contact history. The discovery phase maps your available data to the specific model being built and identifies gaps before development starts. Projects with 12-24 months of labelled historical data produce the most reliable models.