One of many revenue-and-trust use cases. This example fuses CDR, CRM, device, and payment signals to catch novel fraud against institutional memory instead of a stale rule list.
Proves these Lumina features
Global telecom fraud reached roughly $41.8 billion in 2025. International revenue-share fraud, SIM-box bypass, and subscription fraud each run into the billions. Crucially, about 42% of the top fraud methods are account-manipulation, not technical signatures, which means they require reasoning over behavior, not pattern matching.
Static rules and thresholds always lag. Fraudsters adapt faster than rules ship, and the signal that exposes them, spanning call records, CRM, device, and payment, is scattered across silos that must be fused fast to matter.
Traditional approaches were not built for the scale and complexity of modern operations.
Threshold and rule engines only catch known patterns. A novel SIM-swap or subscription-fraud variant looks normal until it is in the rulebook, which is always after the loss.
When 42% of fraud is account-manipulation, the tell is anomalous behavior across systems, not a signature a rule can match.
CDR, CRM, device, and payment data sit apart. By the time they are joined for an investigation, the fraud has run its course.
When your detection logic is both a competitive edge and a compliance artifact, you cannot have it buried inside a model you do not control or cannot audit.
Three layers of intelligence working together: reasoning agents, proactive detection, and multi-agent deliberation.
Agents fuse CDR, CRM, device, and payment signals and reason about behavior, not just thresholds. Your fraud logic stays outside the model, owned and auditable, through Separation of Logic, so the rules that are your edge remain yours.
Every calculation is SQL you can verify. No black box.
Investigating MSISDN +1-555-0142... SIM changed 2h ago from new device (IMEI first-seen today) + 14 international calls to high-risk IRSF ranges in 40 min + payment method added 1h before SIM swap → SIM-swap + IRSF pattern, confidence HIGH → Each signal links to its source record
EMERGING PATTERN: 23 new accounts (last 48h) share a device-IMEI prefix + bill to 3 payment cards Call profile: 90% to 4 premium-rate ranges No existing rule matches. Behavioral cluster, p=0.001 → Likely subscription-fraud ring forming. Flagged before chargeback.
The Radar scans the whole base for behavioral anomalies that no existing rule describes: improbable call-destination mixes, velocity spikes, device-and-account churn. It surfaces the new pattern as it emerges.
The Radar surfaces issues the operator didn't know to look for. Before they become incidents.
Fraud, risk, and customer agents deliberate before anything touches an account. The customer agent guards against false positives on legitimate subscribers. The output is a governed recommendation, a case routed to your fraud team, never an automatic suspension.
The output is grounded in facts (SQL results), not hallucination. Every recommendation carries a full audit trail.
Fraud Agent: MSISDN +1-555-0142 matches SIM-swap + IRSF, recommend suspend Risk Agent: $4,200 exposure in 40 min, time-sensitive Customer Agent: 6-year account, but the device and call pattern broke completely 2h ago, consistent with takeover → Recommendation: hold international + flag for fraud-team callback. Analyst confirms before suspension.
Lumina addresses the four strategic problems that hold operators back.
Reason over the full subscriber base in near real time, catching behavioral fraud that sampling and thresholds miss.
Cut losses against a $41.8B industry problem by catching novel variants in the window where intervention still prevents the loss.
Cortex accumulates every fraud pattern ever seen, so each emerging variant is checked against institutional memory, not a rule list someone has to maintain.
Every fraud signal links to its source record, and every action is a governed recommendation to your team. Your fraud IP stays outside the model, owned and audit-ready.
Specialized AI agents that power this workflow.
Fraud Investigator
Fuses CDR, device, and payment signals to reason about SIM-swap, IRSF, bypass, and subscription fraud.
Exposure Analyst
Quantifies financial exposure and time-sensitivity to prioritize cases that matter most.
Customer Guard
Protects legitimate subscribers from false positives by reasoning about account history and intent.
This is just one of many use cases
Fraud detection & management is one example of how Lumina reasons on operational data. Across Telecom, every domain has use cases where AI agents can add value.
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View all Telecom use cases