Category: Revenue assurance

Webinar Recap: Preventing Revenue Leakage – Core vs. Intelligent Network Reconciliation

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Webinar Recap: Preventing Revenue Leakage – Core vs. Intelligent Network Reconciliation

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:

  1. 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.
  2. 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.

  1. 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.

  1. 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:

  1. Aggregating CDRs from PGW.
  2. 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:

📺 Watch the Webinar Recording Now

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

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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:

  1. Voice: Matching CDRs from the Mobile Switching Center (MSC) with the IN system to ensure proper billing for calls.
  2. SMS: Aggregating fragmented SMS records for accurate reconciliation.
  3. 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.

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

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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, how the process can be implemented for both voice (MO) and SMS (MO) services, the challenges encountered, and how Big Data tools like Apache Spark can significantly enhance the efficiency and accuracy of this 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.

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.