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Quest Training

Telecom Fraud Management & Revenue Protection Training Course

Shows how to detect telecom fraud and stop revenue leakage through revenue assurance, billing controls, interconnect checks and data analytics.

TCS · Telecommunications CybersecurityAll Levels#2804ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Telecommunications & Satellite TechnologyCategories:Telecommunications Cybersecurity

Course Overview

This course develops practical skills in detecting telecom fraud, preventing revenue leakage, and strengthening financial controls across telecommunications operations. It covers common fraud scenarios, revenue assurance processes, billing controls, interconnect settlements, and the use of data analytics to identify suspicious activity. Participants learn to investigate fraud indicators, assess financial exposure, implement preventive controls, and establish monitoring procedures that protect revenue while maintaining service quality and regulatory compliance.

Learning Objectives

  • Identify common telecom fraud types and revenue leakage sources.
  • Apply fraud detection and risk assessment techniques.
  • Evaluate billing accuracy, rating, and revenue assurance controls.
  • Analyse traffic, subscriber, and transaction data for suspicious patterns.
  • Strengthen controls for roaming, interconnect, and premium-rate services.
  • Develop fraud investigation and incident escalation procedures.
  • Establish fraud and revenue protection KPIs.
  • Prepare corrective action plans to reduce financial losses.

Who Should Attend

Telecom fraud management and revenue assurance specialists. Billing and interconnect settlement professionals. Finance and commercial operations teams. Risk, compliance, and internal audit professionals. Telecom security and fraud investigation analysts. Data analysts supporting revenue protection. Managers responsible for billing integrity and financial controls.

Learning Outcomes

  • Classify common telecom fraud schemes and their financial impact.
  • Identify revenue leakage across the service and billing lifecycle.
  • Analyse usage records, call detail records (CDRs), and billing exceptions.
  • Apply controls to reduce fraud and billing discrepancies.
  • Investigate suspicious activity and document findings.
  • Prioritise fraud risks according to financial exposure and likelihood.
  • Develop monitoring dashboards and escalation procedures.
  • Measure the effectiveness of fraud prevention and revenue recovery.

Course Outline

Day 1

Telecom Fraud Types and Risk Assessment

  • Telecom fraud landscape and its operational and financial impact.
  • Subscription fraud, identity misuse, SIM-related fraud, and account takeover.
  • International Revenue Share Fraud (IRSF), International Traffic Pumping, and premium-rate fraud.
  • Roaming fraud, interconnect abuse, and unauthorised service usage.
  • Fraud risk assessment and identification of vulnerable processes.
  • Fraud controls across customer onboarding, service activation, and account management.
  • Practical Exercise: Analyse simulated fraud cases, identify control weaknesses, and rank risks by financial impact and likelihood.
Day 2

Revenue Assurance and Billing Controls

  • Revenue lifecycle from service provisioning to billing, invoicing, and collection.
  • Revenue leakage caused by provisioning errors, rating discrepancies, and incomplete usage records.
  • Billing mediation, charging systems, and reconciliation processes.
  • Call detail records (CDRs), usage records, and transaction validation.
  • Interconnect and roaming settlement verification.
  • Revenue assurance controls, exception reporting, and discrepancy investigation.
  • Practical Exercise: Review sample usage and billing records to identify missing charges, incorrect tariffs, and potential revenue leakage.
Day 3

Fraud Detection and Data Analytics

  • Fraud indicators in call patterns, subscriber behaviour, usage volumes, and transaction records.
  • Rule-based detection, thresholds, anomaly detection, and risk scoring.
  • Analysing CDRs and usage data to identify unusual destinations, traffic spikes, and abnormal activity.
  • Correlating subscriber, network, billing, and transaction information.
  • False-positive management and prioritisation of high-risk alerts.
  • Fraud dashboards, alert workflows, and investigation documentation.
  • Practical Exercise: Analyse a simulated telecom dataset to identify suspicious traffic patterns, prioritise alerts, and recommend investigation actions.
Day 4

Fraud Investigation and Revenue Recovery

  • Fraud case management, evidence collection, and investigation planning.
  • Establishing incident timelines and tracing suspicious transactions.
  • Coordinating investigations across network operations, billing, finance, security, and customer service.
  • Containment measures, account restrictions, and service suspension procedures.
  • Revenue recovery, dispute management, and settlement correction.
  • Root cause analysis and corrective action tracking.
  • Practical Exercise: Investigate a simulated fraud incident, estimate financial exposure, and prepare a case report with containment and recovery recommendations.
Day 5

Fraud Prevention Strategy and Performance Management

  • Designing an integrated fraud management and revenue protection framework.
  • Strengthening preventive controls, access management, and approval processes.
  • Establishing KPIs for fraud losses, detection time, recovery rates, and revenue leakage.
  • Monitoring control effectiveness and recurring discrepancies.
  • Developing fraud risk registers and prioritised mitigation plans.
  • Continuous improvement, staff awareness, and coordination with external partners.
  • Practical Exercise: Develop a fraud and revenue protection action plan covering priority risks, preventive controls, monitoring indicators, recovery procedures, and implementation responsibilities.

Upcoming Dates

Selected date

Amsterdam · 16 – 20 Aug 2027 · Classroom

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