Telecommunications companies and communication service providers (CSPs) face an ongoing challenge in protecting their networks against various types of fraud. The telecommunications industry is not just battling traditional threats but also new, innovative fraud mechanisms, propelled by evolving technologies. According to the Communications Fraud Control Association (CFCA) Global Fraud Loss Survey 2023, the industry reported an estimated $38.95 billion in fraud losses for 2023. The top five fraud types contributing to this loss are:
Interconnect Bypass fraud, or SIM box fraud, is a persistent threat that demands vigilant attention, particularly in regions with high call termination costs. Fraudsters exploit these high-cost routes to profit from voice traffic, resulting in significant financial losses for telecom operators.
Beyond the immediate financial losses and security risks, SIM Box fraud disrupts the very fabric of a functioning telecommunications network. The use of SIM boxes introduces irregular call patterns, leading to network congestion and degrading the quality of service which can affect the overall user experience. This ultimately undermines customer trust and satisfaction, potentially impacting the company’s brand reputation and market share.
As CSPs enhance their detection and prevention capabilities, fraudsters continuously adapt to counter-fraud measures. Traditional methods of SIM box detection often involve Test Call Generation (TCG), extensive manual analysis of call detail records (CDRs) and monitoring of voice and data traffic patterns for anomalies. These methods are time-consuming and resource-intensive, costly and often constrained by the capacity to swiftly process substantial volumes of data.
With the evolving fraud tactics, coupled with the sheer volume of data generated by modern networks, there is a need for communication service providers (CSPs) to adopt advanced machine learning techniques in their fraud detection strategies.
Machine learning algorithms can analyze vast amounts of data in real-time, identifying complex patterns and anomalies indicative of fraudulent activity. By leveraging machine learning, CSPs can enhance their ability to detect and mitigate SIM Box fraud promptly and effectively, safeguarding revenue streams and network integrity.
SIM Box fraud poses a significant threat to Communication Service Providers (CSPs), exploiting the discrepancy between international and local call rates to divert revenue. This fraudulent scheme operates covertly, presenting itself as legitimate communication while diverting profits from unsuspecting CSPs. A detailed examination of the mechanics behind this financial threat reveals the intricate processes involved.
Central to SIM Box fraud is a device known as a SIM Box, which appears as ordinary network equipment but is capable of housing numerous SIM card slots. Fraudsters populate these slots with a large number of prepaid SIM cards, often acquired through illicit means. These SIM cards form the basis of the fraudulent operation, providing seemingly legitimate access points for further exploitation.
VoIP (Voice over Internet Protocol) gateways work in conjunction with SIM Boxes and play a pivotal role in the fraud. They intercept incoming international calls and convert them into digital form. This conversion from traditional voice communication to VoIP format is essential, as it allows for efficient transmission over the internet, setting the stage for the next phase of the scheme.
The VoIP calls are then routed through the various SIM cards in the SIM Box. This routing process, which appears random, serves a strategic purpose: each call is sent through a different SIM card, giving the impression of legitimate traffic originating from individual mobile subscribers within the CSP’s network. This elaborate misdirection is central to the fraud, as it helps to avoid detection by simulating normal call activity.
The interconnect bypass fraud exploits the difference between local and international charges, allowing fraud operators to pay lower rates or avoid charges to the CSPs while billing international rates to the source. This results in the caller paying higher international rates, but the local telecommunications operator does not receive the appropriate charges, leading to revenue losses.
Traditional methods of fraud detection struggle to keep up with the evolving tactics of SIM Box fraudsters. The vast amounts of data generated by modern telecommunications networks make it exceedingly difficult for security personnel to manually identify the anomalies associated with SIM Box activity. Furthermore, fraudsters continuously adapt their strategies, frequently switching SIM cards and altering call patterns to evade detection. This dynamic approach blurs the lines between legitimate and fraudulent traffic, making VoIP-based SIM Box operations appear as standard call activity within the network.
Neural Technologies’ advanced machine learning (ML) and artificial intelligence (AI) solution, ActivML, offers a powerful solution to combat SIM box fraud, leveraging sophisticated analytics with Explainable AI and automated actions to eliminate SIM box fraud threats. With its MLOps capability, this empowers CSPs to build, train, and deploy fraud detection models seamlessly, even without specialized technical expertise. ActivML solution key features include:
ActivML's self-learning structured analytical profiling is a data-driven algorithm that doesn’t rely on static rules-based approaches and can constantly iterate and optimize itself without the need for hard coding. This provides CSPs with an understanding of the SIMs behavior and results are showcased through a graphical visualization.
The unconstrained anomaly detection feature provides a comprehensive analysis to pinpoint SIM behaviors that deviate significantly from the norm. The results are supported by detailed explanations using the Explainable AI (XAI) analysis tool. Through the use of predictive analytics, ActivML can continuously learn and adapt to automatically identify unusual patterns and emerging fraud risks, empowering CSPs to proactively deploy preventive measures.
Transparency is paramount in fraud detection, and ActivML's Explainable AI (XAI) analytics provide CSPs with graphical tools to understand the rationale behind fraud alerts. This transparency fosters trust among CSP staff and facilitates informed decision-making. Additionally, ActivML's continuous learning from live data ensures that fraud detection models remain up-to-date and effective, adapting to emerging fraud patterns and staying ahead of evolving techniques.
The implementation of ActivML has yielded promising results in the fight against SIM box fraud. With a remarkable 98% anomaly detection accuracy, the majority of fraudulent activities are promptly identified and addressed. This high level of accuracy significantly reduces revenue losses and strengthens CSPs' fraud detection capabilities, making ActivML an indispensable tool in the ongoing battle against SIM box fraud.
SIM box fraud remains a critical challenge for CSPs, but our work with major telecom operators reveals how advanced machine learning and artificial intelligence solutions powered by Neural Technologies’ ActivML technology and extensive industry experience can help eliminate that threat even as it evolves in attempts to avoid detection.
Download our case study to learn more on how ActivML tackles SIM Box fraud.