Vijan.AI
Case StudiesTelecom & MediaFraud Prevention

Telecom & Media

Revenue Assurance & Fraud Prevention

5 autonomous agents detect revenue leakage and prevent fraud. 98% revenue capture rate.

5 Autonomous Agents98% Revenue Captured
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Agentic AI Workflow

5 autonomous agents audit revenue, reconcile interconnect, and ensure billing accuracy

The Challenge

Revenue leakage and fraud were silently draining millions from the bottom line

A telecom operator estimated $28M in annual revenue leakage from mediation errors, unbilled usage, and incorrect rate application. Only 2% of CDR records were sampled for billing accuracy, meaning most errors went undetected.

Fraud losses were growing at 15% year-over-year, with SIM swap fraud and subscription fraud being the fastest-growing categories. Investigation teams took 72 hours to compile evidence for fraud cases, by which point financial damage was done. The fraud team could investigate only 40% of flagged cases.

The operator needed comprehensive revenue assurance and real-time fraud prevention.

The Solution

Agents that detect leakage, classify fraud, investigate cases, recover revenue, and improve models

Vijan.AI deployed 5 agents. The Leakage Detector compares every CDR against billing records, identifying mediation errors, unbilled usage, and rate discrepancies. The Fraud Classifier identifies SIM swap, subscription fraud, and international revenue share fraud patterns in real-time. The Investigation agent assembles evidence packages with customer history, transaction patterns, and network data. The Recovery agent initiates collection workflows for confirmed fraud and billing corrections for leakage. The Model Trainer feeds confirmed cases back to improve detection accuracy continuously.

Autonomous Agents

How each agent reasons, decides, and acts

Step 1 · Revenue

Revenue Assurance Agent

Revenue Leakage Detection & Anomaly Analysis

Audits call detail records and billing transactions to detect revenue leakage, rating errors, and unbilled services, autonomously flagging discrepancies and triggering corrections.

Input

CDRs, billing transactions, pricing catalogs, historical revenue

Output

Leakage alerts, error reports, recovery estimates, correction workflows

  • Calls CDR audit engine to compare rated vs. actual usage and pricing
  • Calls revenue ML model to detect anomalies in billing patterns and ARPU trends
  • Autonomous decision: flag for re-rating, issue credit, or escalate to finance
  • Routes rating errors to Billing Automation agent for correction

Step 2 · Billing

Billing Automation Agent

Automated Rating, Invoicing & Collections

Rates usage, generates invoices, and processes payments, autonomously handling plan changes, prorations, and late-payment collections to maximize on-time revenue.

Input

CDRs, plan details, payment history, dunning rules

Output

Invoices, payment confirmations, dunning notices, collection cases

  • Calls rating engine to apply tariffs, discounts, and overages to usage data
  • Calls invoice generator to create and send bills via email, SMS, and portal
  • Autonomous decision: auto-charge credit card, send dunning notice, or suspend service
  • Routes payment data to Interconnect agent for carrier settlement reconciliation

Step 3 · Interconnect

Interconnect Settlement Agent

Carrier Interconnect Settlement & Dispute Resolution

Reconciles interconnect traffic and settlements with partner carriers, autonomously validating NRTRDE records, disputing overcharges, and remitting payments.

Input

NRTRDE files, carrier invoices, traffic logs, contract rates

Output

Settlement reports, dispute claims, payment approvals, audit logs

  • Calls NRTRDE platform to ingest carrier traffic records and settlement data
  • Calls carrier API to validate termination rates and traffic volumes
  • Autonomous decision: approve payment, dispute variance, or escalate to legal
  • Routes settlement costs to Cost Attribution agent for P&L allocation

Step 4 · Cost

Cost Attribution Agent

Service Cost Allocation & Profitability Analysis

Allocates interconnect, network, and support costs to products and customer segments, autonomously generating profitability reports and identifying loss-making services.

Input

Settlement costs, network expenses, support labor, subscriber segments

Output

Cost allocations, profitability reports, loss analysis, pricing recommendations

  • Calls general ledger to retrieve network and operations expense details
  • Calls cost model to allocate expenses by product line, region, and customer tier
  • Autonomous decision: flag unprofitable segments, recommend price adjustments, or defer analysis
  • Routes cost data to Regulatory Fee agent for USF and E911 calculations

Step 5 · Regulatory

Regulatory Fee Agent

Regulatory Fee Calculation & Remittance

Calculates USF, E911, and state telecom fees based on revenue and subscriber counts, autonomously filing reports and remitting payments to regulatory authorities.

Input

Revenue data, subscriber counts, fee rates, filing deadlines

Output

Fee calculations, regulatory filings, payment remittances, compliance reports

  • Calls FCC API for current USF contribution factors and E911 fee schedules
  • Calls fee calculator to apply rates to interstate revenue and eligible lines
  • Autonomous decision: file quarterly reports, remit payments, or request deadline extensions
  • Routes compliance confirmations to finance for audit trails and regulatory records

Results

Measurable impact within 90 days of deployment

98%

Revenue Captured

Revenue capture rate improved from 94% to 98%. $22M in previously undetected leakage identified and corrected.

92%

Fraud Prevented

Fraud losses reduced 92%. Real-time detection blocks SIM swap attempts before completion.

< 4hrs

Investigation Time

Fraud evidence packages compiled in under 4 hours vs. 72 hours manually. 100% of cases investigated.

$24M

Annual Recovery

Combined annual recovery from corrected leakage, prevented fraud, and improved billing accuracy.

Implementation

From pilot to production in 12 weeks

Week 1-4

Agent Design & Tool Integration

Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.

Week 5-8

Shadow Mode & Autonomous Tuning

Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.

Week 9-12

Full Autonomous Deployment

Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.

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