Telecom CDR (Call Detail Record) Decoding and Analysis

Did you know that modern operators use CDR analytics to detect fraud with over 97% accuracy, automatically flag revenue leakages in real time, and standardize multi-operator traffic into lawful-interception-ready CDR feeds for large-scale government analysis?

Course Overview

The Telecom CDR (Call Detail Record) Decoding and Analysis course by Rcademy is designed to equip telecom billing teams, revenue assurance professionals, fraud investigators, security intelligence analysts, compliance officers, network engineers, data analysts, mediation system administrators, and law enforcement professionals with comprehensive understanding of telecom network architecture, call routing mechanisms, and how voice, SMS, and data sessions generate CDRs across 2G/3G/4G/5G networks. Participants gain expert knowledge of CDR lifecycle including collection, validation, correlation, normalization, and aggregation, decoding multiple CDR formats from diverse vendor equipment, and advanced CDR analysis for fraud detection and revenue assurance.​

Without specialized CDR analysis training, professionals may struggle to decode ASN.1 and vendor-proprietary formats, perform systematic data cleansing and validation, apply machine learning for anomaly detection, execute switch-to-bill reconciliation, or use CDR data for lawful interception workflows, limiting their ability to support accurate billing, detect revenue leakage, and maintain regulatory compliance. This comprehensive course provides a structured path to mastery across CDR fundamentals, collection and data flow, format decoding, validation and cleansing, correlation and aggregation, fraud detection methods, revenue assurance procedures, and lawful interception capabilities, preparing attendees to lead CDR processing and analytics initiatives.​

Real-world cases show how an Indonesian mobile operator applied K-Means and DBSCAN clustering algorithms to real CDRs containing confirmed fraud events, with confusion matrix evaluation showing K-Means achieved 97.2% accuracy outperforming DBSCAN in modeling fraud behavior.

Studies also show that a large Namibian network operator deployed Adapt IT’s RAFMS to continuously monitor CDR streams and related data, highlighting anomalies in real time with drill-down capabilities enabling analysts to quickly investigate cases and trace full CDR trails, reducing manual workload and shortening the cycle from leakage detection to recovery.

Take charge of your CDR analytics expertise. Enroll now in the Rcademy Telecom CDR Decoding and Analysis course to master the decoding and analysis skills that drive accurate billing and fraud detection.

Who Should Attend?

The Telecom CDR (Call Detail Record) Decoding and Analysis course by Rcademy is ideal for:

• Telecom billing teams and revenue assurance professionals
• Fraud investigators and security intelligence analysts
• Compliance officers and regulatory specialists
• Network engineers and operations staff
• Data analysts and business intelligence professionals
• Mediation system administrators
• Billing system engineers
• Law enforcement and government agencies
• CDR processing specialists
• System integrators implementing CDR solutions
• Quality assurance engineers
• Network operations center analysts
• Roaming and interconnect billing specialists
• Forensic investigators using CDR data
• Anyone seeking comprehensive CDR analysis certification

What are the Training Goals?

The main objectives of the Telecom CDR (Call Detail Record) Decoding and Analysis course are to enable professionals to:

• Understand comprehensive telecom network architecture, call routing mechanisms, and how voice, SMS, and data sessions generate CDRs across 2G/3G/4G/5G networks.
• Master CDR lifecycle: collection, validation, correlation, normalization, and aggregation for complete and accurate billing records.
• Decode multiple CDR formats (ASN.1, XML, binary, delimited text) from diverse vendor equipment (MSC, SGSN, gNB, PCEF) with precision and speed.
• Apply systematic data cleansing, validation, and filtering techniques to ensure CDR quality for downstream billing and analysis processes.
• Perform advanced CDR analysis for fraud detection, anomaly identification, and suspicious activity patterns using machine learning and statistical methods.
• Execute revenue assurance procedures: switch-to-bill reconciliation, gap detection, loss quantification, and revenue leakage recovery.
• Leverage CDR data for regulatory compliance, lawful interception, security intelligence, and cross-border settlement.
• Analyze CDRs for network optimization, capacity planning, customer behavior insights, and service quality monitoring.

How Will This Training Course Be Presented?

At Rcademy, the extensive focus is laid on the relevance of the training content to the audience. Thus, content is reviewed and customised as per the professional backgrounds of the audience.

The training framework includes:

• Expert-led lectures by senior billing and revenue assurance professionals using audio-visual sessions
• Hands-on exercises with CDR decoding tools and format conversion utilities
• Interactive workshops for fraud detection using K-Means and DBSCAN clustering algorithms
• Case studies covering Indonesian mobile operator fraud detection, Namibian operator RAFMS deployment, and multi-operator lawful interception
• Practical labs for switch-to-bill reconciliation, gap analysis, and CDR correlation exercises

The theoretical part of training is delivered by an experienced professional from the relevant domain, using audio-visual presentations. This CDR-focused approach ensures professionals translate theory into practical workflows through format decoding, validation procedures, anomaly detection modeling, and systematic revenue assurance methodologies.​

This comprehensive certification model ensures participants gain both CDR fundamentals and hands-on proficiency to immediately apply decoding and analysis expertise in billing, fraud detection, revenue assurance, and compliance roles.​

Register now to experience a rigorous, hands-on learning journey designed to equip you for leading CDR processing, fraud detection, and revenue assurance projects.

Course Syllabus

Module 1: Telecom Network Introduction

• Evolution of telecom networks: PSTN/PLMN basics to 2G/3G/4G/5G architectures.​
• Network element roles: MSC, VLR, HLR, SGSN, GGSN, gNB, PCEF, and their CDR generation responsibilities.​
• Circuit-switched voice, packet-switched data, and IMS-based multimedia sessions overview.​
• Call routing, session establishment, and inter-network interconnection fundamentals.​
• Protocol stacks: SS7, Diameter, GTP signaling relevant to CDR collection points.​

Module 2: CDR Fundamentals and Types

• Definition and purpose: CDR as primary billing data source.​
• CDR lifecycle: generation, collection, validation, correlation, normalization, storage, analysis.​
• Types of CDRs: voice call records (COD, COLT), SMS records, data session records (IPDR, GPRS).​
• Partial CDR correlation and complete call record aggregation across network elements.​
• Voice CDR fields: calling party, called party, timestamps, duration, termination point, service type, billing data.​

Module 3: CDR Collection and Data Flow

• CDR collection points in network: switches, session servers, packet gateways, RAN elements.​
• Collection mechanisms: offline (file-based) and online (stream-based) CDR delivery.​
• CDR buffering, queuing, and loss prevention techniques.​
• Mediation platform role: reception, pre-validation, temporary storage, and forwarding to billing systems.​
• Integration with billing and CRM systems for invoice generation.​

Module 4: CDR Formats and Decoding

• ASN.1 (Abstract Syntax Notation 1) encoding: structure, tagging, BER/DER encoding techniques.​
• XML-based CDR formats: schema definitions, hierarchical structure, parsing methods.​
• Binary CDR formats: vendor-proprietary structures, byte-level interpretation, endianness.​
• Delimited text formats (CSV, pipe-delimited): field parsing and data extraction.​
• Converting between formats: codec tools, mapping, and transformation techniques.​

Module 5: CDR Validation and Cleansing

• Validation rules: mandatory field checks, data type validation, range/bounds checking.​
• Syntax validation: format compliance, encoding correctness, character set verification.​
• Semantic validation: logical consistency, inter-field relationships, business rule compliance.​
• Data quality metrics: completeness, accuracy, consistency, timeliness assessment.​
• Cleansing techniques: duplicate removal, error correction, outlier handling.​

Module 6: CDR Correlation and Aggregation

• Identifying related CDRs from multiple sources for single logical call/session.​
• Correlation keys: calling party, called party, timestamps, transaction ID matching.​
• Handling partial CDRs and incomplete information scenarios.​
• Aggregating multi-leg calls (conference, call transfer, roaming scenarios).​
• Building complete call records from originating and terminating network CDRs.​

Module 7: CDR Normalization and Standardization

• Normalizing disparate vendor formats to common internal representation.​
• Time standardization: timezone conversion, clock synchronization, timestamp alignment.​
• Number normalization: E.164 format, removing prefixes/suffixes, country code standardization.​
• Service code mapping and harmonization across network elements.​
• Unit conversion: durations (seconds vs. minutes/ticks), data volumes (bytes vs. MB/GB).​

Module 8: Charging Data Records (CDR) Specifics

• Long call monitoring and late-start charging for extended sessions.​
• Roaming charges and international billing data in CDRs.​
• Usage-based charging: minutes, SMS, data volume, and tier-based billing.​
• Toll-free and special service number handling in CDRs.​
• Supplementary service charges: call forwarding, conferencing, premium content.​

Module 9: Fraud Detection and Anomaly Analysis

• Fraud patterns: traffic pumping, subscription fraud, SIM cloning, call pattern abuse.​
• Machine learning approaches: K-Means, DBSCAN clustering for fraud detection.​
• Statistical anomaly detection: outlier identification using mean/standard deviation, Z-scores.​
• Rule-based fraud detection: threshold violations, unusual patterns, behavioral analytics.​
• Call pattern analysis: community detection, destination concentration, temporal anomalies.​

Module 10: Revenue Assurance and Gap Detection

• Revenue assurance methodology: detection, correction, prevention framework.​
• Switch-to-bill reconciliation: comparing network-generated CDRs with billed amounts.​
• Gap detection: identifying missing or lost CDRs, duplicate records, undercharging scenarios.​
• Loss quantification: calculating revenue impact of detected gaps and errors.​
• Reclamation procedures and root cause analysis for revenue leakage.​

Module 11: Lawful Interception and Security Intelligence

• CDR analysis for lawful monitoring and government-mandated intercepts.​
• Cell ID and location-based analysis using CDR geographic data.​
• IMEI tracking and device fingerprinting for suspect identification.​
• Communication pattern analysis: social network mapping and relationship detection.​
• Call chain analysis and link analysis for intelligence investigations.​

Module 12: International CDR Standards and Cross-Border Billing

• TAP (Telecom Application Protocol) for roaming data exchange.​
• IPDR and xDR standards for data session records.​
• Inter-carrier billing formats and interconnect settlements.​
• Regulatory requirements: GDPR, CASBAA, local compliance standards.​
• International roaming billing and currency conversion in CDRs.​

Module 13: CDR Tools and Platforms

• Mediation platforms (Amdocs, Ericsson, Huawei, Nokia) for CDR processing.​
• ETL tools for CDR extraction, transformation, and loading.​
• Data warehouse and analytics platforms for CDR-based business intelligence.​
• Visualization tools for call pattern and fraud ring detection.​
• Python, SQL, and scripting techniques for CDR analysis and reporting.​​

Module 14: Network Optimization Using CDR Analytics

• Traffic analysis and network utilization insights from CDRs.​
• Capacity planning and forecasting using historical CDR data.​
• Peak hour analysis and load balancing optimization.​
• Service quality metrics: dropped call rate, connection success, duration analysis.​
• Roaming patterns and inter-network performance assessment.​

Module 15: Practical Labs and Case Studies

• Lab: Decoding sample ASN.1, XML, and binary CDRs from diverse vendors.​
• Lab: Validating, cleansing, and normalizing raw CDR datasets.​
• Lab: Correlating partial CDRs and assembling complete call records.​
• Lab: Fraud detection using clustering algorithms on CDR datasets.​
• Lab: Switch-to-bill reconciliation and gap analysis exercises.​
• Case studies: real-world fraud detection, revenue leakage recovery, and cross-border billing scenarios.

Training Impact

The impact of CDR Decoding and Analysis training is visible in how operators deploy integrated revenue assurance and fraud management platforms, enable multi-operator lawful interception using standardized CDR feeds, and apply machine learning to real CDRs for fraud detection.​

Large Namibian Network Operator – Integrated Revenue Assurance and Fraud Management with Adapt IT RAFMS

Implementation: A large Namibian network operator required a platform where potential revenue loss, suspicious transactions, or unusual customer activity would automatically be flagged and tracked using a unified Revenue Assurance and Fraud Management System (RAFMS). Adapt IT implemented RAFMS as a single dashboard-driven platform that continuously monitors revenue streams, cross-checks CDRs against billing and provisioning systems, and uses anomaly detection to identify discrepancies and risky behavior. The solution consolidates revenue assurance and fraud detection into a single environment, allowing teams to move from high-level KPIs into detailed CDR views using drill-down capabilities.​

Results: The operator gained real-time visibility into revenue leakages and fraud patterns, could prioritize investigations based on risk scoring, and significantly shortened the time from issue detection to correction, improving profitability and compliance. RAFMS automatically flags potential leakage cases and provides investigators with an end-to-end CDR trail, reducing manual workload and supporting structured switch-to-bill reconciliation, gap detection, and loss quantification, exactly the revenue assurance workflows covered in the course.​

Multi-Operator Lawful Interception – Cubro EXA64100 CDR Distillation for Government Big Data Analysis

Implementation: In a lawful interception deployment involving three major telecom providers (mobile and fixed), Cubro’s EXA64100 Network Packet Broker was implemented as the central data distillation and delivery platform between operators and a government big data analytics environment. Telecom providers allowed access to their mobile and fixed networks and provided fixed network point codes and Circuit Identification Codes, while Cubro captured raw traffic and distilled it into standardized CDRs with accurate field population for both mobile and fixed communications. Authorized government agencies then stored and analyzed this standardized CDR dataset under legal mandates for fraud detection and law enforcement.​

Results: The EXA64100 handled massive traffic volumes from multiple networks without dropping data and ensured compliant, secure CDR delivery to government systems, with all collected sessions retained for downstream filtering and analysis. The standardized CDR corpus with consistent field definitions across operators enabled more effective fraud detection, lawful interception, and security intelligence workflows, illustrating how robust CDR collection, normalization, and secure delivery skills from the course are applied in national-scale surveillance and compliance environments.

Indonesian Mobile Operator – CDR Fraud Detection Using K-Means and DBSCAN

Implementation: A fraud research study used real CDR data from an Indonesian mobile operator that had experienced significant fraud incidents in 2009 and 2018, with each CDR containing fields such as source number, destination number, call start time, duration, cost, and destination country. Researchers applied K-Means and DBSCAN clustering algorithms to identify anomalous traffic patterns and evaluated performance against a labeled dataset of actual fraud cases provided by the operator, using confusion-matrix-based metrics such as accuracy, precision, and recall.​

Results: The evaluation showed that K-Means achieved higher accuracy than DBSCAN in modeling fraud behavior on the operator’s CDRs, with K-Means reaching an accuracy of about 0.97 compared to approximately 0.90 for DBSCAN. The study concluded that these unsupervised machine learning methods are suitable for telecommunications fraud detection and recommended their adoption in operators’ fraud management frameworks, directly reflecting the course modules on feature engineering, K-Means/DBSCAN clustering, confusion matrix evaluation, and CDR-driven fraud analytics.​

Be inspired by how operators turn raw CDR data into revenue protection and fraud prevention at scale. Join the Rcademy Telecom CDR Decoding and Analysis course to gain the decoding and analytics skills that support revenue assurance and lawful interception.