Revenue Assurance (RA) and Fraud Management (FM) are critical functions for telecom operators aiming to protect their network, revenue streams and minimize financial losses. Ensuring these teams have access to the right data is essential for identifying discrepancies, addressing vulnerabilities, and implementing robust controls. Below is a detailed guide on the type of data to rovide to RAFM Teams to enhance revenue assurance and prevent revenue leakage effectively.
Type of Data to Provide to RAFM Teams by Telecom operators
1. Call Detail Records (CDRs)
Why They Are Essential: CDRs provide detailed information about every call made or received on the network, including time, duration, source, destination, and cost. RAFM teams use CDRs to identify discrepancies between billed and actual usage.
Key Attributes:
- Call start and end times
- Caller and recipient numbers
- Call type (e.g., local, international, roaming)
- Network element IDs (e.g., MSC,OCS)
- Applied rates and chargesUse Case: Reconciliation of CDRs against billing system data to detect under-billing or over-billing issues.
2. Data Usage Records
Why They Are Essential: Ensuring that all data usage is accurately captured and billed is crucial. Data usage records provide details on internet and app usage patterns by subscribers.
Key Attributes:
- Data session start and end times
- Volume of data transferred (upload/download)
- Session type (e.g., streaming, browsing)
- Associated costs and plans
Use Case: Reconciliation of data session records with charging systems to identify unbilled usage.
3. SMS Records
Why They Are Essential: SMS remain significant revenue sources, particularly in regions with lower internet penetration. RAFM teams need to ensure proper billing for all messaging services.
Key Attributes:
- Sender and recipient numbers
- Message type (e.g., domestic, international, bulk)
- Time of delivery
- Billing rates
Use Case: Cross-verification of SMS records with billing platforms to detect revenue leakage from promotional offers or network issues.
4. Subscriber Information and Profiles
Why They Are Essential: Accurate subscriber data ensures that customers are billed according to their subscribed plans, discounts, and usage patterns.
Key Attributes:
- Customer name and account details
- Subscription type (prepaid/postpaid)
- Plan details (e.g., data caps, call minutes, SMS bundles)
- KYC compliance data
Use Case: Reconciliation of subscription data with billing plans to detect discrepancies like incorrect plan activations or unregistered users.
5. Network Event Logs
Why They Are Essential: Network event logs provide insights into the functioning of core and intelligent network elements. These logs are crucial for identifying technical glitches that may lead to revenue leakage.
Key Attributes:
- Network element activity logs
- Error codes and failure records
- Timestamped records of events
Use Case: Identifying dropped calls or failed SMS deliveries that are not billed despite usage.
6. Billing System Data
Why They Are Essential: RAFM teams need access to billing system data to ensure alignment between what customers are charged and their actual usage.
Key Attributes:
- Billed amounts and invoices
- Applied discounts and promotions
- Payment records
Use Case: Auditing billing data against CDRs and subscription plans to ensure billing accuracy.
7. Mediation System Data
Why They Are Essential: The mediation system acts as the bridge between network-generated data and the billing system. Any discrepancies here can lead to revenue leakage.
Key Attributes:
- Raw data from network elements
- Processed data passed to billing systems
- Rejected or dropped records
Use Case: Reviewing mediation logs to identify lost data records that could impact billing.
8. Fraud Alerts and Patterns
Why They Are Essential: Fraudulent activities can lead to significant revenue losses. RAFM teams need detailed fraud data to identify and mitigate risks promptly.
Key Attributes:
- Detected fraud types (e.g.,Simbox bypass,CLI bypass fraud)
- Location and time of fraud occurrences
- Subscriber details involved in suspicious activities
Use Case: Cross-referencing fraud patterns with network and billing data to detect systemic vulnerabilities.
9. Interconnect and Roaming Data
Why They Are Essential: Revenue from interconnect and roaming services is susceptible to discrepancies due to differing billing systems between operators.
Key Attributes:
- Interconnect call/SMS records
- Roaming agreements and charges
- Reconciliation reports from partner operators
Use Case: Auditing interconnect and roaming data to ensure accurate settlements and prevent disputes.
10. Complaint and Dispute Records
Why They Are Essential: Customer complaints about billing inaccuracies can highlight gaps in the revenue assurance process.
Key Attributes:
- Complaint details
- Resolution steps and timelines
- Financial impact of resolved disputes
Use Case: Using complaint data to identify and address recurring issues in billing and revenue collection processes.
How Sharing the Right Data Ensures Effective Revenue Assurance
Sharing accurate and comprehensive data across departments is crucial for ensuring seamless revenue assurance processes. Here’s how it makes a difference:
Seamless Reconciliation of Records:
- Accurate data sharing ensures that network-generated data (e.g., CDRs, data usage records) aligns with billing and subscriber records.
- Helps RAFM teams identify and resolve discrepancies promptly, reducing delays in revenue collection.
Billing Accuracy and Transparency:
- Comprehensive datasets allow RAFM teams to cross-verify usage records against billing system data.
- Minimizes errors such as over-billing, under-billing, or unbilled usage, improving customer trust and satisfaction.
Enhanced Fraud Detection:
- Sharing data across teams allows for cross-referencing fraud alerts with network activity and billing logs.
- Enables faster identification of patterns, such as SIM fraud or unauthorized usage, and allows immediate mitigation.
Improved Decision-Making:
- Access to shared, accurate data provides RA/FM teams with actionable insights to support strategic decisions.
- Supports proactive measures by identifying trends and anomalies before they escalate into significant issues.
Streamlined Collaboration:
- Fosters collaboration between RAFM, IT, and network teams by providing a unified view of operations.
- Reduces silos and ensures all stakeholders are aligned in revenue assurance efforts.
To empower RAFM teams, our solutions S-ONE RA and S-ONE FRAUD provide comprehensive dashboards and analytics tailored to monitor, reconcile, and act on key operational data.
S-ONE RA delivers real-time revenue assurance analytics through customizable dashboards and automated reporting. With features such as detailed call detail records (CDRs) analysis, data usage monitoring, and billing system reconciliation, S-ONE RA enables teams to swiftly identify discrepancies and prevent revenue leakage.
S-ONE FRAUD focuses on fraud monitoring, offering robust analytics to detect and analyze irregular patterns in transaction data. By highlighting suspicious activities—such as potential Simbox fraud, Wangiri, CLI bypass, and other anomalies—S-ONE FRAUD equips RAFM teams with the insights needed to secure the network and protect revenue.
Together, these solutions streamline data sharing across departments and support proactive decision-making. They ensure RAFM teams have a unified view of critical data, enhancing collaboration and operational efficiency.
For more information, download our brochures:
Download S-ONE RA Brochure
Download S-ONE FRAUD Brochure
For a live demonstration of S-ONE RA’s capabilities, including its powerful dashboards, Book a Call today and see how we can transform your revenue assurance processes.
Conclusion
Providing RAFM teams with comprehensive and accurate data is the foundation for effective revenue assurance and fraud prevention. By ensuring access to CDRs, data usage records, subscriber profiles, and other key datasets, telecom operators can proactively identify and resolve revenue leakage issues. Moreover, fostering collaboration between network, IT, and RAFM teams can further strengthen controls and enhance financial performance.
To succeed in this mission, operators must also invest in advanced analytics tools and automated reconciliation systems to process and analyze data efficiently. Revenue assurance is not just about preventing losses but also about building a robust framework that ensures long-term profitability and customer trust.
Telecom operators face constant challenges in ensuring accurate reconciliation between core and intelligent network elements for preventing revenue leakage and ensuring seamless service delivery. During our recent webinar, Preventing Revenue Leakage: Core vs. Intelligent Network Reconciliation, we explored:
- The importance of reconciling data between core network elements (MSC, SMS-C, GGSN, SGSN) for revenue assurance.
- Common challenges encountered in Voice, SMS, and Data reconciliation.
- Practical demonstrations on reconciling data effectively between core network elements and the Intelligent Network (IN) to prevent revenue leakage and ensure data accuracy.
We also engaged in a vibrant Q&A session, addressing critical questions from participants. Here’s a recap of the key questions and our expert responses:
1. Why Don’t Postpaid Accounts Show on OCS in MSC vs OCS Reconciliation?
Answer: There are two scenarios:
- OCS manages both prepaid and postpaid accounts: In this case, OCS generates Call Detail Records (CDRs) for all accounts (prepaid, hybrid, postpaid), and these CDRs are reconciled with MSC data.
- OCS manages only prepaid and hybrid accounts: In this scenario, OCS doesn’t generate CDRs for postpaid accounts. Instead, another system (Offline Billing) collects postpaid CDRs directly from MSC for rating. The reconciliation process must include these rated records from Offline Billing to complete the reconciliation.
2. How to Handle Pay-As-You-Go (PAYG) and Bundles for Prepaid Accounts?
Answer: Prepaid subscribers their voice, SMS, and data usage can be charged as follows:
- PAYG (Pay-As-You-Go) charges are deducted from the main account for airtime.
- Free resources purchased via main account airtime or mobile money wallet.
For both scenarios the reconciliation process traces the sms, voice and data usages in both MSC and OCS. However, reconciling free resource usage vs. bundle purchased at the OCS level accuracy depends on how operators manage balances:
- If operators use separate balances for each bundle, the reconciliation process is straightforward and can verify the accuracy of the consumption of the bundle.
- If operators accumulate free resources from different bundles into a single balance, reconciliation becomes more complex.
3. How Do You Manage Different Operator File Structures in CDR Analysis Tools (e.g., Ericsson vs Huawei)?
Answer: The reconciliation process is designed to be scalable and vendor-agnostic. All vendor CDRs are mapped into a unified schema, ensuring compatibility and streamlined analysis across different systems.
4. How Can VoLTE Reconciliation Be Effectively Done?
Answer: VoLTE reconciliation involves challenges related to both voice and data portions of traffic. While a deep dive into this topic is planned for a dedicated webinar, initial considerations include correlating VoLTE data and voice records across network elements and billing systems.
5.Est-il possible d’identifier les numéros qui peuvent émettre des appels mais pas identifiés sur IN (ENG:Is It Possible to Identify Numbers That Can Make Calls but Are Not Registered on the IN?
Answer: Yes, it is possible. The reconciliation process must access Know Your Customer (KYC) data from CRM to enrich CDRs from both MSC and OCS. This enables filtering for non-identified subscribers engaging in traffic (voice, SMS, or data).
6. Comment détecter des numéros créés sur HLR et non sur IN (ENG:How Can We Detect Numbers Created on HLR but Not on IN?)
Answer: This can be achieved by reconciling dumps from HLR and OCS. By comparing these datasets, it’s possible to identify MSISDNs enabled on the HLR but not on the OCS.
7.Please can you guide us on how to effectively reconcile PGW vs OCS
Answer: The PGW usually generates a lot of intermediate CDRs with a unique correlation ID called the Charging ID, which is also reported in data charging CDRs from OCS. Effective reconciliation involves:
- Aggregating CDRs from PGW.
- Joining aggregated data with OCS records using MSISDN and Charging ID.
Key Takeaways from the Webinar
- Data reconciliation is essential for ensuring revenue assurance and preventing revenue leakage.
- Challenges vary across network elements, but scalable, vendor-agnostic solutions simplify the process.
- Accurate reconciliation requires enriching datasets with external information like KYC data.
- Collaboration between network teams and revenue assurance specialists is crucial for success.
Access the Webinar Recording
Missed the webinar or want to revisit specific sections? Watch the full recording for detailed insights and practical demonstrations:
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If you have further questions or need personalized support for your reconciliation challenges, don’t hesitate to reach out to our team. Let’s work together to safeguard your revenue and optimize your operations!
How Successful Reconciliation Reduces Revenue Leakage and Boosts Network Efficiency
Ensuring revenue assurance and operational efficiency has become a top priority for telecom operators. With the increasing complexity of telecom services—spanning voice, SMS, data, and now 5G networks—the risks of revenue leakage have never been higher. Reconciliation, the process of aligning data across core and intelligent network elements, emerges as a powerful tool to combat these challenges.
The Scope of Revenue Leakage in Telecom
Revenue leakage is a widespread issue, costing telecom operators up to $135 billion annually, according to industry studies. These losses arise from various sources:
- System integration issues between core network elements (e.g., MSC, GGSN, SGW) and intelligent network systems (IN).
- Manual errors during reconciliation processes.
- Outdated or misconfigured billing systems, resulting in missed charges or incorrect invoices.
For example, discrepancies in call detail records (CDRs) or usage detail records (UDRs) can lead to unbilled services or overcharges, which not only affect revenue but also erode customer trust.
Why Reconciliation Matters
Reconciliation ensures that the data flowing between different telecom network components is accurate, consistent, and up-to-date. It’s particularly critical for services like:
- Voice: Matching CDRs from the Mobile Switching Center (MSC) with the IN system to ensure proper billing for calls.
- SMS: Aggregating fragmented SMS records for accurate reconciliation.
- Data Services: Aligning data usage records from GGSN/SGW with billing systems to prevent unbilled data usage.
Core Benefits:
- Accurate Billing: Operators can ensure that every service is accounted for, reducing overcharges or undercharges.
- Fraud Prevention: Reconciliation helps identify anomalies, such as SIM box fraud or call bypass incidents.
- Operational Efficiency: Automation in reconciliation processes reduces manual intervention and minimizes errors.
- Regulatory Compliance: Accurate reporting ensures that operators meet strict compliance requirements.
How Reconciliation Boosts Efficiency
By eliminating inconsistencies, reconciliation streamlines telecom operations. Here’s how it drives efficiency:
- Real-Time Data Matching: Advanced tools like Apache Spark allow operators to match millions of records in real time, detecting discrepancies instantly.
- Automation: Robotic Process Automation (RPA) eliminates repetitive tasks, freeing up teams to focus on strategic initiatives.
- Scalability: With telecom networks expanding to accommodate 5G and IoT, reconciliation tools can handle higher data volumes and new service models.
- Cross-Department Collaboration: By aligning billing, operations, and network teams, reconciliation fosters a unified approach to revenue assurance.
The Role of Big Data and AI
Emerging technologies like Big Data and Artificial Intelligence (AI) are revolutionizing reconciliation processes. AI-powered tools enable operators to:
- Predict Revenue Risks: Machine learning algorithms analyze patterns to detect potential revenue leaks before they occur.
- Streamline Fraud Detection: Behavioral analytics can identify irregularities, such as unusually high data usage or unexpected call patterns.
- Optimize Service Delivery: Real-time insights from Big Data ensure that services are delivered as promised, maintaining high customer satisfaction.
A Practical Example
Consider a scenario where an operator authorizes a data session but fails to reconcile the actual usage due to delays in record synchronization. Without proper reconciliation, the operator could miss billing for part of the session, leading to significant revenue loss. Implementing a reconciliation system that uses real-time data synchronization would resolve this issue efficiently.
Watch our Webinar: Preventing Revenue Leakage: Core vs. Intelligent Network Reconciliation.
Want to learn more about data reconcialition between core and intelligent network? Watch our insightful webinar
📺 Watch the Webinar Recording Now
Key Takeaways from the Webinar
- Data reconciliation is essential for ensuring revenue assurance and preventing revenue leakage.
- Challenges vary across network elements, but scalable, vendor-agnostic solutions simplify the process.
- Accurate reconciliation requires enriching datasets with external information like KYC data.
- Collaboration between network teams and revenue assurance specialists is crucial for success.
Conclusion
Successful reconciliation is not just about preventing revenue leakage; it’s about creating a resilient, customer-focused telecom ecosystem. By leveraging advanced tools, automating processes, and fostering collaboration across departments, telecom operators can unlock higher revenue potential while boosting operational efficiency.
Adopting these best practices ensures that as the telecom industry evolves, operators are well-equipped to tackle its challenges and seize its opportunities.
The Importance of CDR Reconciliation between MSC and IN for Voice and SMS
Call Detail Records (CDRs) serve as a fundamental building block for tracking, billing, and analyzing various communication services like voice calls and SMS. These records are generated by different network elements such as the Mobile Switching Center (MSC) and the Intelligent Network (IN). However, discrepancies between these records can lead to serious issues, including revenue leakage, fraud, and data inconsistencies. This blog post explores why CDR reconciliation between MSC and IN is crucial. it outlines how to perform reconciliation for voice and SMS services, highlights common challenges, and shows how big data tools like Apache Spark can streamline the process.
Why CDR Reconciliation is Important?
CDR reconciliation is not just a technical exercise; it is a crucial component of an effective revenue assurance process. Ensuring that all CDRs from different network nodes match perfectly is essential for several reasons. From maximizing revenue capture to ensuring compliance, a reliable reconciliation process can prevent numerous issues that telecom operators face on a daily basis. Let’s delve into the key reasons why this process is indispensable:
- Revenue Assurance: Reconciliation ensures that all billable events are captured and billed correctly. Discrepancies between MSC and IN CDRs can lead to revenue leakage, where services are provided but not billed accurately.
- Financial Integrity: Reconciliation validates the consistency of call records across different network elements, ensuring that the data used for billing, analysis, and reporting is accurate, thereby safeguarding financial integrity.
- Operational Efficiency: Identifying missing or duplicate records during reconciliation helps in detecting errors in the network, such as incomplete calls or unbilled events, which could lead to potential revenue loss and operational inefficiencies.
- Regulatory Compliance: Telecom operators must often report accurate traffic data to regulatory authorities. CDR reconciliation helps in ensuring the accuracy of these reports, thereby meeting regulatory requirements and avoiding penalties.
Achieving CDR Reconciliation for Voice (MO) and SMS (MO)
Implementing an effective CDR reconciliation process requires a systematic approach to match records from MSC and IN. While the goal is to ensure that both sets of records align perfectly, the actual process involves several steps and considerations, especially when dealing with multiple fields like msisdn, imsi, caller, called, timestamp, and sometimes a unique correlation ID. Here’s how you can achieve a robust reconciliation process for voice and SMS services:
Reconciliation Process for Voice (MO) between MSC and IN:
- Identify Key Fields: The main fields to consider for reconciliation are msisdn, imsi, caller, called, timestamp, and a unique correlation ID.
- Intermediate CDRs Aggregation: MSC often generates intermediate CDRs for the same call, especially for long-duration calls that are split into smaller segments. These intermediate CDRs must be aggregated based on caller, called, and timestamp to create a complete call record before reconciling with IN CDRs.
- Unique Correlation ID: Ideally, there should be a unique identifier that links the CDRs from MSC and IN for the same call. This ID ensures that the call records can be matched accurately across the two systems.
- Timestamp Matching: In cases where a unique correlation ID is not available, we can use a combination of fields like caller, called, and timestamp to identify matching records. However, time differences between MSC and IN systems can pose challenges.
Reconciliation Process for SMS (MO) between MSC and IN:
- Identify Key Fields: Similar to voice, important fields for SMS reconciliation include msisdn, imsi, sender, receiver, timestamp, and a unique correlation ID (if available).
- Aggregation of Long SMS Messages: For SMS, particularly long messages that are split into multiple segments (e.g., concatenated SMS), it is important to aggregate these segments into a single CDR based on sender, receiver, and timestamp before proceeding with reconciliation. This ensures that the entire message is accounted for correctly, avoiding partial billing or data discrepancies.
- Matching Criteria: After aggregation, it is crucial to match the sender and receiver numbers along with the timestamp to accurately reconcile the SMS records between MSC and IN.
Challenges in CDR Reconciliation
Despite the critical need for CDR reconciliation, the process is often challenging. From the absence of a unique correlation ID to time discrepancies between different network elements, telecom operators often face significant obstacles in achieving accurate and consistent CDR reconciliation. Understanding these challenges is essential to developing strategies to mitigate them. Here are some of the most common issues faced during the reconciliation process:
- Lack of Unique Correlation ID: Many times, there is no unique correlation ID between MSC and IN, making it difficult to directly match the records. In such cases, using a combination of msisdn, caller, called, and timestamp becomes necessary.
- Intermediate CDRs for Voice Calls: For long-duration calls, MSC generates multiple intermediate CDRs. These need to be aggregated to form a single complete call record before any reconciliation can be performed with IN CDRs. Failure to do so can result in mismatches and inaccuracies.
- Segmented SMS Messages: Long SMS messages that are split into multiple segments must be aggregated before reconciliation. Missing or incomplete segments can lead to discrepancies between MSC and IN records.
- Time Difference Between MSC and IN: Even a slight time difference between the CDR generation in MSC and IN can cause mismatches. For instance, if the MSC and IN systems are not synchronized to the same time source, the same event may appear at slightly different times, complicating the reconciliation process.
- Handling Duplicate Records: Sometimes, multiple CDRs are generated for the same event, especially in cases of call setup failures or retries. These duplicates must be filtered out to avoid false discrepancies.
- High Data Volume: The large volume of CDRs generated daily in a telecom network can make reconciliation a resource-intensive and time-consuming process.
How Big Data Tools Like Apache Spark Can Help
With the rise of Big Data technologies, the telecom industry now has powerful tools to handle large-scale data reconciliation more efficiently. Apache Spark, in particular, stands out for its ability to process massive datasets in parallel, making it an excellent choice for CDR reconciliation. Leveraging Spark can not only speed up the reconciliation process but also provide additional capabilities like anomaly detection and real-time analytics. Here’s how Spark can transform the reconciliation process:
- Scalability and Speed: Spark can handle large volumes of data efficiently with its in-memory processing capabilities, making it ideal for processing millions of CDRs quickly.
- Distributed Processing: Spark’s distributed architecture allows it to parallelize the reconciliation process across multiple nodes, significantly speeding up the comparison of records from MSC and IN.
- Data Transformation: Spark provides powerful APIs for data manipulation, making it easier to preprocess the CDR data (e.g., filtering out duplicates, handling missing values, aligning timestamps, and aggregating intermediate CDRs or segmented SMS messages) before reconciliation.
- Advanced Analytics: Spark’s integration with MLlib allows for the application of machine learning techniques to detect anomalies or patterns in CDR data that could indicate issues such as revenue leakage or fraud.
- Automated Matching: Spark can be used to automate the matching process based on multiple criteria, such as caller, called, timestamp, and imsi, and flagging records that do not match for further analysis.
Watch our Webinar: Preventing Revenue Leakage: Core vs. Intelligent Network Reconciliation.
Explore how telecom operators reconcile CDRs to protect revenue. Learn from real-world use cases and get expert insights.
📺 Watch the Webinar Recording Now
Key Takeaways from the Webinar
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Reconciliation prevents revenue loss and ensures billing accuracy.
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Vendor-agnostic solutions simplify reconciliation across diverse networks.
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Enriching CDRs with KYC and external data improves results.
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Success depends on collaboration between network and assurance teams.
Conclusion
CDR reconciliation between MSC and IN is a vital process for telecom operators, enabling them to maintain accurate billing, detect fraud, and ensure compliance. Although challenges like the absence of a unique correlation ID and time differences exist, these can be mitigated with proper data processing and advanced tools like Apache Spark. By adopting such big data solutions, telecom operators can not only streamline the reconciliation process but also gain valuable insights to drive business decisions.
Ensuring Accuracy in Data Reconciliation between CGSN and IN for 2G/3G and 4G Networks
Introduction
Data usage records are logged by various network nodes, such as SGSN/GGSN in 2G/3G networks and SGW/PGW in 4G networks. These records, known as data session EDRs (Event Detail Records), capture critical information about data sessions, including the volume of data used, session duration, and charging details. Meanwhile, the Intelligent Network (IN) records the billing details associated with these data sessions. Reconciliation between between CGSN and IN sources is essential to ensure accuracy in billing, revenue assurance, and network management. In this blog post, we will explore the importance of reconciling these data records, the challenges involved, and how Big Data tools like Apache Spark can streamline this process.
Why Data Records Reconciliation is Important
- Accuracy in Data Billing: Each data session, whether in a 2G/3G or 4G network, must be accurately billed to the customer. Discrepancies between the volume of data recorded by the CGSN and the charges recorded by the IN can lead to billing errors, causing revenue loss and customer dissatisfaction.
- Revenue Assurance: Ensuring that all data usage is correctly captured and billed is crucial for preventing revenue leakage. Reconciliation helps identify missing, duplicated, or incorrect records, allowing operators to correct discrepancies proactively.
- Network Performance Monitoring: Reconciliation can also provide insights into network performance by comparing the expected usage (as recorded by SGSN/GGSN or SGW/PGW) with the actual charges. This helps operators in network planning and optimization.
How to Achieve Data Records Reconciliation between CGSN and IN?
- Matching Using MSISDN, IMSI, and Timestamp:
- MSISDN and IMSI are unique subscriber identifiers that link data sessions across network and billing systems.
- The timestamp is a crucial attribute that captures the start and end times of a session. Matching records based on MSISDN, IMSI, and timestamp helps in accurately linking data usage records from CGSN and IN.
- Using a Unique Correlation ID:
- Some systems generate a unique correlation ID for each data session, linking records between the network and billing nodes seamlessly. This ID makes reconciliation straightforward by directly associating each data session with its corresponding billing record.
- However, in many instances, this unique ID is not available, complicating the reconciliation process.
Challenges in Data Records Reconciliation?
- Absence of a Unique Correlation ID:
- When there is no unique ID linking records between CGSN and IN, operators must rely on MSISDN, IMSI, and timestamp for matching. This approach is prone to errors, especially when dealing with sessions that start and stop frequently or overlap.
- Time Synchronization Issues:
- Even a minor time discrepancy between SGSN/GGSN (or SGW/PGW) and IN can lead to unmatched records. These discrepancies can arise due to differences in system clocks, network delays, or processing times.
- To address this, operators often use a time window to match records, where sessions are considered correlated if their timestamps fall within a predefined range, such as ±10 seconds.
- Handling High Volumes of Intermediate Records:
- Data sessions often generate multiple intermediate records, especially during long or fragmented sessions. These records need to be consolidated into a single session record before reconciliation.
- For 4G networks, SGW/PGW may generate separate records for different parts of a session, further complicating the consolidation process.
Leveraging Big Data Tools for Efficient Reconciliation?
- Using Apache Spark:
- Apache Spark’s distributed processing capabilities are ideal for handling large volumes of data records from both CGSN and IN. It allows for efficient matching of records based on multiple keys like MSISDN, IMSI, and timestamp.
- Spark’s in-memory processing reduces latency, enabling near real-time reconciliation, which is critical for maintaining billing accuracy and revenue assurance.
- Consolidating Intermediate Records:
- Spark can aggregate multiple intermediate records into a single session based on MSISDN and IMSI, while applying business rules to filter out duplicates and handle overlaps.
- For example, all records with the same MSISDN and IMSI within a session can be grouped together, and their data volume and duration summed to create a consolidated record.
- Handling Time Differences:
- Spark’s window functions allow for flexible time-based grouping and aggregation. A time window can be defined to match records with slight timestamp differences, accounting for system clock discrepancies between CGSN and IN.
- This helps in accurately correlating records, even when exact timestamps do not match.
- Scaling with Data Growth:
- As data usage continues to grow, the volume of EDRs from SGSN/GGSN and SGW/PGW increases exponentially. Spark’s ability to scale horizontally by adding more nodes to the cluster ensures that reconciliation processes can keep pace with the growing data volumes without compromising performance.
Introducing S-ONE RA: Your Trusted Reconciliation Engine
At Synaptique, we understand the complexity of reconciling data across diverse telecom environments. That’s why we developed S-ONE RA—our powerful, vendor-agnostic reconciliation solution designed to ensure end-to-end accuracy in your telecom data, whether you’re reconciling MSC vs. IN for voice and SMS or CGSN vs. IN for data sessions in 2G, 3G, or 4G networks.
What Makes S-ONE RA Different?
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Multi-Technology Support: Handles CDRs and EDRs from MSC, SMSC, SGSN/GGSN, SGW/PGW, and IN for 2G, 3G, and 4G networks.
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Correlation Without IDs: When correlation IDs are missing, S-ONE RA intelligently matches records using MSISDN, IMSI, and timestamp-based logic.
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Consolidates Intermediate Records: Automatically groups and aggregates intermediate or fragmented records into a single session—perfect for long calls or data sessions with multiple handovers.
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Built on Big Data Tech: Powered by Apache Spark, S-ONE RA processes high volumes of data in near real-time, ensuring performance and scalability.
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Audit Trail and Reporting: Every reconciliation result is logged with explanations, summaries, and drill-down capabilities for audit and compliance purposes.
Whether you’re tackling billing accuracy, revenue assurance, or data integrity challenges, S-ONE RA provides you with the automation, scalability, and intelligence you need.
For a live demonstration of S-ONE RA capabilities, Book a Call today and see how we can transform your revenue assurance processes.
Conclusion
Reconciliation of data records between SGSN/GGSN (or SGW/PGW) and IN is crucial for accurate billing, revenue assurance, and network management. Despite the challenges such as the absence of a unique correlation ID, time synchronization issues, and high volumes of intermediate records, big data tools like Apache Spark provide a robust solution. Spark’s distributed processing, in-memory computation, and advanced aggregation capabilities enable efficient and scalable reconciliation, ensuring data integrity and billing accuracy.
In the next blog post, we will provide a step-by-step guide on implementing a Spark-based data records reconciliation pipeline, complete with code examples and best practices. Stay tuned.
Core network elements and intelligent network (IN) elements play critical roles in managing services like voice, SMS, and data. Understanding how these elements interact is vital for ensuring accurate revenue assurance, which is a key challenge for telecom operators. This post explains the functions of key network components and highlights the importance of reconciliation for managing revenues effectively.
Core Network Elements
- Mobile Switching Center (MSC):
- Role: The MSC is responsible for managing voice calls and SMS in both 2G and 3G networks. It handles the switching and routing of these services, ensuring smooth communication between mobile users and other network types such as the PSTN (Public Switched Telephone Network).
- Key Functions:
- Call setup and teardown.
- Mobility management, including location updates and handovers.
- Generation of Call Detail Records (CDRs) for billing purposes.
- SMS handling between mobile subscribers and external messaging systems.
- Serving GPRS Support Node (SGSN):
- Role: The SGSN is part of the packet-switched (PS) domain in 2G and 3G networks. It is responsible for the delivery of data services, including internet access, over GPRS and UMTS. The SGSN manages the mobility of users and keeps track of their data usage.
- Key Functions:
- Session management for data services.
- Authentication and mobility management within the PS domain.
- Collecting data usage statistics for billing.
- Forwarding data sessions to the Gateway GPRS Support Node (GGSN).
- Gateway GPRS Support Node (GGSN):
- Role: The GGSN is the gateway between the mobile network’s PS domain (managed by the SGSN) and external packet data networks, such as the internet. It assigns IP addresses to mobile users and routes data between the mobile network and the external network.
- Key Functions:
- Management of IP addresses for mobile devices.
- Billing data generation for data services.
- Acting as a liaison between mobile data networks and external packet networks.
- Serving Gateway (SGW) and Packet Data Network Gateway (PGW):
- Role: In 4G LTE networks, the SGW and PGW replace the SGSN and GGSN, respectively, to handle data traffic. The SGW routes and forwards packets between the eNodeB (base station) and external networks. The PGW connects the LTE network to external packet data networks, such as the internet, managing IP address assignment and handling services like billing and policy enforcement.
- Key Functions:
- SGW: Forwarding data traffic within the LTE network and between the LTE network and external networks.
- PGW: Managing mobile user IP traffic, including routing, IP address assignment, and applying policies for billing.
Intelligent Network (IN) Elements
- Intelligent Network (IN):
- Role: The IN manages value-added services like prepaid billing, call forwarding, and other real-time services. The IN interacts with the MSC and other core network elements to provide these services while keeping track of real-time user balances and other account information.
- Key Functions:
- Real-time prepaid charging.
- Execution of customized services like VPNs and number portability.
- Supplementary service control (e.g., balance inquiries, top-ups).
Why Reconciliation is Crucial for Revenue Assurance
Telecom operators must ensure that the records generated by core network elements like the MSC, SGSN, GGSN, SGW, and PGW match the real-time billing information recorded by IN systems. Any discrepancies between these systems can lead to significant revenue leakage. Here’s why reconciliation is essential:
- Accurate Billing: Core elements such as the MSC and SGSN/GGSN generate CDRs for voice, SMS, and data usage. These records must be reconciled with billing information from IN systems, especially for real-time services like prepaid charging. If a mismatch occurs, operators risk revenue loss, either by overcharging or undercharging customers.
- Revenue Assurance: Discrepancies between the traffic data captured by core network elements and the charging data in the IN can lead to missed revenue opportunities. For instance, if the IN fails to register a data session or voice call correctly, the operator may not capture the associated revenue.
- Fraud Prevention: Reconciling data from the core and IN networks also helps detect and prevent fraud, such as bypassing prepaid billing systems or unauthorized use of services. By correlating voice, SMS, and data usage records across systems, operators can identify unusual patterns indicative of fraud, like SIM box fraud or call bypass.
- Ensuring Service Quality: Reconciliation not only ensures proper billing but also enables operators to monitor and maintain the quality of services. By comparing the data from different systems, operators can ensure that services are delivered according to the promised quality and detect any service anomalies.
Watch our Webinar: Preventing Revenue Leakage: Core vs. Intelligent Network Reconciliation.
Want to learn more about data reconcialition between core and intelligent network? Watch our insightful webinar
📺 Watch the Webinar Recording Now
Key Takeaways from the Webinar
- Data reconciliation is essential for ensuring revenue assurance and preventing revenue leakage.
- Challenges vary across network elements, but scalable, vendor-agnostic solutions simplify the process.
- Accurate reconciliation requires enriching datasets with external information like KYC data.
- Collaboration between network teams and revenue assurance specialists is crucial for success.
Conclusion
For telecom operators focused on revenue assurance, reconciling data between core network elements (MSC, SGSN, GGSN, SGW, PGW) and intelligent network elements (IN) is crucial. This process ensures accurate billing, prevents revenue loss, and helps detect fraud, all while maintaining high service quality. Implementing robust reconciliation processes using Big Data Analytics can help operators stay ahead of potential revenue leakage and improve operational efficiency.
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