Category: FRAUD

8 Ways Telecom Operators Can Stop Simbox Fraud Using AI and Machine Learning

by Salwa LAARIFPosted on

SIMBox fraud is one of the most pervasive and costly threats facing telecom operators today. By exploiting SIM boxes to reroute international calls as local calls, fraudsters bypass legitimate interconnect fees, causing significant revenue leakage for operators and compromised service quality. Traditional fraud detection methods are no longer sufficient to combat this sophisticated threat. However, with the power of Artificial Intelligence (AI) and Machine Learning (ML), telecom operators can now detect and prevent SIMBox fraud in real-time. Here are eight ways AI and ML can help stop SIMBox fraud:

1.Real-Time Call Pattern Analysis

SIMBox fraud relies on unusual call patterns, such as a high volume of short-duration calls or a sudden spike in international call traffic routed through local numbers. AI-powered systems can analyze call data records (CDRs), frequency, and anomalies in real-time to forecast potential Simbox activities before they materialize.

to identify these anomalies. Machine learning algorithms can learn normal call behavior and flag deviations that indicate potential SIMBox activity. By detecting these patterns early, operators can block fraudulent calls before they cause significant damage.

2.Real-time Traffic Monitoring

Real-time Traffic Monitoring is essential for promptly identifying and mitigating fraudulent activities. AI systems excel at monitoring call traffic in real-time, instantly flagging suspicious activities. This immediate detection capability is crucial for reducing the window of opportunity for fraudsters.

For example, AI can monitor call routes and identify discrepancies that suggest Simbox usage. By responding swiftly to these alerts, operators can prevent significant losses and maintain the integrity of their networks.

3.Voice Traffic Fingerprinting

AI and ML can be used to analyze the unique characteristics of voice traffic, such as voice quality, latency, and jitter. SIMBox calls often exhibit distinct audio fingerprints due to the rerouting process. Machine learning models can be trained to recognize these subtle differences and distinguish between legitimate and fraudulent calls. This advanced voice traffic analysis ensures that even the most sophisticated SIMBox setups can be detected.

4.Geolocation and Network Behavior Analysis

SIMBox fraudsters often operate across multiple locations, making it difficult to track their activities. AI-driven geolocation tools can analyze the origin and routing of calls to identify inconsistencies. For example, if a local number is receiving an unusually high volume of calls from a single international location, it could indicate SIMBox fraud. Machine learning models can also monitor network behavior, such as IP addresses and device signatures, to detect suspicious activity.

5.Advanced Behavioral Analytics

Understanding network behavior is crucial for distinguishing legitimate activities from fraudulent ones. Advanced Behavioral Analytics powered by machine learning enable telecom operators to comprehend both normal and abnormal behaviors within their networks.

Machine learning algorithms continuously learn from vast datasets, improving their ability to detect even the most subtle signs of fraud. By identifying behavioral anomalies, these systems can alert operators to potential Simbox fraud, facilitating timely intervention and minimizing damage.

6.Automated Fraud Detection and Response

Manual fraud detection processes are time-consuming and often ineffective against rapidly evolving SIMBox schemes. Machine learning models can continuously analyze data, identifying Simbox fraud patterns and issuing real-time alerts. AI-powered systems can automate the entire fraud detection and response process. For example, when a potential SIMBox is detected, the system can automatically block the fraudulent traffic, alert the fraud management team, and generate detailed reports for further investigation. This automation not only improves efficiency but also ensures a faster response to emerging threats and allows telecom operators to allocate resources more efficiently.

By relying on AI for routine monitoring, human analysts can focus on more complex tasks, improving overall operational efficiency.

7.Predictive Analytics for Proactive Fraud Prevention

One of the most powerful applications of AI and ML is predictive analytics. By analyzing historical data, machine learning algorithms can predict future SIMBox fraud attempts based on emerging trends and patterns. This allows operators to take proactive measures, such as blocking suspicious numbers or strengthening network security, before fraud occurs. Predictive analytics transforms fraud detection from a reactive process to a proactive strategy.

8.Proactive Risk Management

Preventing Simbox fraud requires a proactive approach. Proactive Risk Management involves using historical data and machine learning to develop strategies that anticipate and counter future fraud attempts.

AI models can analyze past incidents of Simbox fraud, identify trends, and predict future threats. This foresight enables telecom operators to implement preventive measures, ensuring their networks remain secure. Proactive risk management not only mitigates current fraud risks but also enhances resilience against emerging threats.

Introducing S-One FRAUD: Your ML-Powered SIMBox Fraud Monitoring Solution

S-One FRAUD, a data solution designed to monitor, detect, and block SIMBox fraud in real-time. Leveraging advanced machine learning algorithms, S-One FRAUD provides telecom operators with a comprehensive tool to safeguard their networks and revenue.

Key Features of S-One FRAUD Synaptique:

  • Real-Time Monitoring: Continuously analyzes call traffic to identify and flag suspicious patterns.
  • Voice Traffic Analysis: Detects SIMBox fraud through advanced voice fingerprinting and quality metrics.
  • Geolocation Insights: Tracks call origins and routes to pinpoint fraudulent activities.
  • Predictive Capabilities: Uses historical data to predict and prevent future fraud attempts.
  • Automated Response: Instantly blocks fraudulent traffic and generates actionable reports.

With S-One FRAUD Synaptique, telecom operators can stay ahead of fraudsters, reduce revenue leakage, and ensure a secure network for their customers.

Download the Brochure to Learn More:

Ready to take the next step in combating SIMBox fraud? Download our brochure to explore how S-One FRAUD Synaptique can transform your fraud prevention strategy. 

Conclusion: Staying Ahead of SIMBox Fraud with AI and ML

SIMBox fraud is a constantly evolving challenge, but with the right tools, telecom operators can stay one step ahead. By leveraging AI and machine learning, operators can detect fraudulent activity in real-time, analyze complex patterns, and automate responses to minimize revenue loss. Investing in these advanced technologies is no longer optional—it’s essential for protecting your network and ensuring long-term profitability.

As telecom fraud specialists, we encourage operators to embrace AI and ML as part of their fraud prevention strategy. The future of telecom security lies in intelligent, data-driven solutions that can adapt to the ever-changing tactics of fraudsters.

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

by Salwa LAARIFPosted on

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 RAFM teams to enhance revenue assurance and prevent revenue leakage effectively.

Type of Data Should Telecom Operators Provide to RAFM Teams

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

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

by Salwa LAARIFPosted on

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.

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

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.

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:

  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!