Intelligent Telecom Operations: Fighting Fraud and Supporting Customers with Agentic AI

Telecom operators move fast. Every second, they process thousands of customer interactions, network events, call records, and transactions. But when it comes to detecting fraud or delivering timely support, speed alone isn’t enough.

The real challenge is awareness.

Operators must be able to detect threats before they escalate and respond to customers before frustration builds. Traditional systems can’t do that. They’re built for throughput, not intelligence.

This is where agentic AI changes the game.

By embedding intelligent agents that understand context, analyze unstructured logs, and interact with customers in real time, telecom providers can shift from reacting after the fact to responding as events unfold.

This article explores how 0to60.ai, in partnership with Databricks, is making that shift real, with agentic AI solutions for fraud detection and customer engagement already in action.

Telecom operations generate immense volumes of unstructured data, from call logs and network diagnostics to support tickets and CRM records. Hidden in that data are patterns of fraud, customer complaints, service outages, and billing issues. But most systems can't surface insights fast enough to take meaningful action.

Here’s where the gaps show up:

• SIM swaps and identity fraud go undetected until damage is done
  
• Support teams work with incomplete data and scripted workflows

• Outages are reported by customers before they're recognized internally
  
• CRM systems operate in silos, detached from network activity
  
  

The result is a reactive operation that exposes providers to revenue loss, regulatory risk, and poor customer satisfaction.

At 0to60.ai, we’ve implemented a dual-agent solution: one focused on real-time fraud detection, the other on intelligent customer support. Together, they deliver proactive control and better experiences.

1. Real-Time Fraud Detection Agents

These agents operate continuously, analyzing live streams of call records and service activity using Databricks Lakehouse as the core data engine.

What they do:

• Identify suspicious activity patterns, such as rapid SIM changes, high-risk access attempts, and irregular call behavior
  
• Apply LLM-powered models to unstructured logs and call data
  
• Use vector search to correlate behavior with past fraud cases
  
• Trigger alerts and automatically open investigation cases in the CRM
  
  

The result is faster detection, fewer missed events, and more efficient fraud mitigation — without relying on batch processes.

2. Customer Support Agents for Billing and Service

Alongside fraud detection, we deployed conversational agents that handle high-volume customer interactions related to billing, usage, and service outages.

What they handle:

• Answering common billing and usage questions based on real-time account data
  
• Providing outage notifications tailored to a customer’s location and plan
  
• Logging unresolved cases directly into the CRM, complete with SLA tracking and escalation paths
  
  

These agents don’t just reduce support load — they ensure customers get faster, more relevant answers without being bounced between channels.

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This solution was built using a tightly integrated stack designed for scale and speed:

• Databricks Lakehouse ingests and unifies call logs, support tickets, and CRM records in real time
  
• Agent Bricks run both fraud detection and conversational models using structured and unstructured data
  
  

Vector search enables agents to find correlations between past fraud patterns and current network activity

0to60.ai handles:

• Streaming ingestion and data pipeline orchestration
  
• Fraud detection model integration
  
• Alert workflows and SLA monitoring
  
• CRM integration and audit trail generation
  
  

This architecture enables real-time decision-making without disrupting existing operations or platforms.