Calculating the Gap: How to Determine Simbox Loss Versus Revenue

Simbox fraud can weaken revenue and disrupt network integrity. At Synaptique, we've seen firsthand how undetected fraudulent call routing can create a significant gap between potential revenue and the actual revenue collected by telecom operators. In this post, we'll explore what simbox loss is, why it matters, and outline a detailed approach to calculating the difference between simbox loss and revenue.

What is Simbox Fraud and Its Impact on Revenue?

Simbox fraud occurs when fraudsters use devices known as simboxes to route international calls as local ones. By bypassing official interconnect channels, these fraudulent schemes result in revenue losses for telecom operators. Although simbox operations might create what seems like additional call volume, it represents lost revenue potential instead of genuine growth.

Key Impacts of Simbox Fraud:

• Revenue Leakage: Fraudulent calls do not pay the higher international tariffs but are billed at lower domestic rates, leading to significant revenue loss.
• Network Congestion: Illegitimate call traffic can overburden network resources, leading to degraded service quality for real users.
• Operational Costs: Additional resources may be required to manage fraud detection, mitigation, and customer support issues arising from fraudulent activities.

Defining the Revenue Gap: What Are We Calculating?

To identify and quantify the financial impact of simbox fraud, we must calculate the difference between the expected revenue (if all calls had been routed legitimately) and the actual revenue (the reduced revenue from fraudulent routing).

Formula Overview

A straightforward approach is to use the following formula:

Simbox Loss = Expected Revenue – Actual Revenue

• Expected Revenue: This represents the revenue that should have been earned if no fraud had taken place. It generally uses the international call tariffs or proper billing rates.
• Actual Revenue: This is the revenue collected under fraudulent conditions, where simbox fraudsters have rerouted calls to incur lower charges or even none at all.

The difference between these figures gives you an estimate of the financial gap created by simbox fraud.

How to Calculate Expected and Actual Revenue

1. Calculating Expected Revenue

To derive the expected revenue, consider the following steps:

• Identify Legitimate Call Volume: Start by determining the volume of calls that should have been routed through legitimate channels. This can be done using historical data, industry benchmarks, or predictive modeling.
• Determine the Proper Tariff: Understand the international call tariffs or correct billing rates that are expected for the type of calls in question.
• Apply the Formula:

Expected Revenue = Legitimate Call Volume × Correct Billing Rate

Example:

If an operator should have processed 1,000 calls at an international rate of $0.50 per minute for an average call duration of 3 minutes, the expected revenue is:

1,000 × 3 × 0.50 = $1,500

2. Calculating Actual Revenue

Actual revenue is easier to pinpoint since it comes from billing data:

• Extract Billing Data: Retrieve the revenue data generated from the calls that were subject to simbox fraud. This data might reflect lower domestic rates or discounted rates applied to fraudulent calls.
• Aggregate Fraudulent Call Data: Sum the revenue from all fraudulent transactions.

Example:

Example: If the same 1,000 calls were billed at $0.20 per minute due to fraudulent rerouting, the actual revenue is:

1,000 × 3 × 0.20 = $600

3. Determining the Revenue Gap (Simbox Loss)

Using the examples:

Simbox Loss = $1,500 − $600 = $900

This $900 represents the revenue lost due to simbox fraud for that period.

What Data and Tools Do You Need?

Essential Data Sources:

• Call Detail Records (CDRs): Detailed logs of every call detailing start and end times, duration, call routing information, etc.
• Billing Records: Data from the financial system that shows how much revenue was collected per transaction.
• Network Traffic Logs: Data that helps verify routing discrepancies and identify anomalous call patterns.

Recommended Tools:

• Big Data Platforms (e.g., Apache Spark): To handle large volumes of data for real-time analysis and reconciliation.
• Revenue Assurance Systems: Tools specifically designed to compare expected and actual revenue streams.
• AI and Machine Learning Solutions: For pattern recognition, anomaly detection, and predictive analytics to refine your reconciliation process.
• Custom Dashboards: For visualizing key metrics in real-time, enabling rapid identification and resolution of revenue discrepancies.

How to Present the Findings to Stakeholders

Once you've computed the revenue loss caused by simbox fraud, the next critical step is to communicate this data effectively to stakeholders:

• Visualization: Use graphs, charts, and tables to clearly depict the difference between expected and actual revenue.
• Reports: Generate detailed reports that include key metrics, trends, and anomalies. Include actionable insights and potential remedies.
• Impact Statements: Explain the financial impact in plain language, linking the data to tangible business outcomes such as reduced profitability, increased operational costs, or compromised customer trust.
• Recommendations: Outline remediation strategies—such as advanced fraud detection, better network monitoring, and enhanced revenue assurance systems—to bridge the gap.

Introducing S-ONE RA: Revolutionizing Revenue Assurance Systems

At Synaptique, we specialize in helping telecom operators transform revenue assurance into a real-time, proactive function. Our solutions for revenue assurance and fraud monitoring, integrates seamlessly into your telecom infrastructure, offering:

• Automated Reconciliation: Align expected and actual revenue data effortlessly.
• Real-Time Alerts: Detect fraudulent activities instantly and take immediate corrective actions.
• Customizable Dashboards: Visualize all relevant KPIs such as transaction volume, success rates, and processing times.
• Predictive Analytics: Leverage AI and machine learning to forecast fraud patterns and prevent revenue loss before it occurs.

With our solutions you gain a comprehensive view of your revenue streams and can quickly identify discrepancies caused by simbox fraud. This ensures more accurate billing, improved customer trust, and stronger financial performance.

Conclusion

Mobile money providers and telecom operators face increasing challenges from sophisticated simbox fraud schemes. Accurately calculating the revenue gap requires a detailed, data-driven approach and the right set of tools. By leveraging advanced technologies like AI, machine learning, and big data analytics, operators can bridge the gap between expected and actual revenue, securing financial integrity and enhancing overall service quality.

Investing in a mature Revenue Assurance System not only protects your revenue but also provides critical insights for proactive decision-making. With the right data, tools, and strategic approach, you can significantly mitigate the financial impact of simbox fraud and ensure a secure, profitable operational environment.

Stay ahead of fraud. Protect your revenue. Empower your future.