US telecom provider boosts network anomaly detection by 42% with AI expert training

The provider sought to strengthen its AI-powered anomaly detection system to catch sophisticated network threats and outages faster.

Their goals were to:

• Detect subtle anomalies before they escalated into customer-impacting events
• Reduce false positives that slowed down response times
• Improve fraud detection, including SIM-swap and international call routing schemes
• Standardize defenses across both 5G and legacy networks

The company's existing detection systems were falling short of keeping pace with the scale and complexity of network threats.

• Current anomaly detection caught only 38% of early-stage incidents
• False positives consumed 60% of engineer review time
• Fraudulent call routing went undetected in $42M annual losses
• Outages in rural areas took 4.5 hours on average to identify
• AI models trained on lab data failed in real-world, noisy environments
• Competitors were achieving 2x faster outage response times with better monitoring

CleverX partnered with the provider to create a specialist-driven AI training program designed for network reliability and fraud prevention.

Expert recruitment:

• 18 network security specialists mobilized, including:
  • 7 telecom engineers with expertise in 5G/VoLTE signaling
  • 6 cybersecurity analysts skilled in intrusion detection
  • 5 fraud prevention experts from top global carriers
• Average 12 years of telecom security experience
• All had direct experience with FCC and telecom compliance standards

Technical framework:

• Developed anomaly detection models across traffic, signaling, and billing data
• Built graph analytics to trace fraud across SIM and device networks
• Created cross-domain monitoring covering 5G, LTE, and broadband infrastructure
• Implemented automated root-cause analysis pipelines for faster incident response

Quality protocols:

• Established false positive reduction methods using multi-layer validation
• Deployed simulated outage scenarios across 50 representative cell sites
• Implemented continuous monitoring dashboards with drill-down analytics
• Created incident escalation protocols aligned with NIST standards

Within just 6 weeks, the telecom provider saw measurable improvements across anomaly detection, fraud prevention, and operational efficiency.

• AI models began detecting subtle early-warning signals missed by previous systems
• Fraudulent traffic patterns were uncovered before financial exposure escalated
• Outage detection latency dropped significantly across all network tiers
• Engineers spent more time resolving incidents rather than triaging false alarms

Efficiency gains:

Network operations became faster and more responsive.

• Reduced average incident detection time from 4.5 hours → 40 minutes
• Cut false positives by 36%, freeing engineer bandwidth
• Increased automated triage coverage from 22% → 69%
• Improved fraud case review throughput by 41%

Quality improvements:

AI-driven monitoring became more accurate and reliable.

• Achieved 42% improvement in anomaly detection rate
• Improved detection of SIM-swap fraud by 55%
• Increased precision on billing anomalies from 0.62 → 0.87 AUC
• Reduced undetected service degradations by 47%

Business impact:

The initiative delivered measurable financial and operational returns.

• Prevented $28M in fraud-related losses annually
• Reduced SLA breach penalties by $6.2M
• Avoided 3 major outages that would have impacted 1.4M customers
• Improved customer satisfaction scores by 22%

Strategic advantages:

The telecom strengthened its position as a reliable, secure provider.

• Built reusable fraud-detection models shared across global operations
• Established industry-first AI-powered anomaly detection framework
• Developed internal AI monitoring playbooks for 5G rollout
• Recognized by a telecom industry council for innovation in network resilience