Tag: Revenue Leakage

What Type of Data Should Telecom Operators Provide to RAFM Teams to Ensure Effective Revenue Assurance and Prevent Revenue Leakage?

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What Type of Data Should Telecom Operators Provide to RAFM Teams to Ensure Effective Revenue Assurance and Prevent Revenue Leakage?

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 types of data operators should provide to RA/FM teams to enhance revenue assurance and prevent revenue leakage effectively.

 

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. RA/FM 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. RA/FM 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: RA/FM 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. RA/FM 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.

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. 

 

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.