Understanding Telecom’s Vulnerability to Fraud
Fraud has long been an established threat to business revenue. Recent data from the Federal Trade Commission reveals that consumers reported losing over $10 billion to fraud in 2023, the first time losses have hit this milestone. This represents a 14% increase compared to the reported losses in 2022.
Various types of fraud pose ongoing threats to enterprises today, from cybercrime endangering data security to evolving risks within telecom operations. Addressing these challenges demands a comprehensive strategy for fraud detection and prevention, grounded in understanding the root causes of threats and implementing robust protective measures.
5 Common Types of Fraud Affecting Telecom
- Subscription Fraud: Involves fraudsters using false identities to obtain telecom services without the intention of paying.
- Wangiri Fraud: Fraudsters trick users into returning missed calls to premium-rate numbers, resulting in exorbitant charges.
- SIM Box Fraud: Involves the use of SIM boxes to bypass international call charges, leading to significant revenue loss.
- Vendor / Dealers Fraud: Deceptive practices by unethical dealers that exploit or abuse partner agreements for personal gain, compromising legitimate sales efforts.
- International Revenue Share Fraud (IRSF): Exploits revenue-sharing agreements by generating high volumes of calls to premium numbers controlled by the fraudsters.
According to recent findings of Communications Fraud Control Association (CFCA), subscription fraud, credit mule fraud, PBX fraud, account takeover, and service/equipment abuse collectively account for 51% of reported fraud incidents. This represents a notable shift from previous years, where tactics such as spoofing, call-back schemes, SMS phishing, pharming, subscription fraud, and IP PBX hacking dominated the landscape.
Subscription fraud had been highlighted as a prominent issue driven by identity fraud and technological advancements. The adoption of new technologies by both service providers and fraudsters, alongside changes in customer engagement processes and global economic conditions, has exacerbated these challenges across various markets.
The digitalization of customer interactions introduces vulnerabilities as not all users are equally proficient with digital interfaces. Fraudsters exploit these gaps by targeting less tech-savvy individuals through phishing, social engineering, and other tactics to gain access to accounts, steal data, or fraudulently acquire devices for resale.
In response, telecom companies must enhance fraud detection capabilities, adapt to evolving tactics, and bolster security measures to safeguard financial integrity and reputation. Advanced AI-powered anomaly detection and machine learning models are crucial for identifying deviations from established patterns indicative of fraudulent activity. These technologies analyze vast datasets, detecting anomalies that traditional methods might overlook, thus fortifying fraud prevention efforts.
Neural Technologies has a long history of working with partners to deliver artificial intelligence (AI) and machine learning (ML) solutions for effective fraud detection and prevention to reduce revenue leakage. That functionality is increasingly vital for smart enterprise planning at a time when vulnerabilities, and fraud itself, are on the rise.
Embracing AI-Driven Fraud Detection and Prevention
Telecommunications is at the forefront of dynamic environments, facing both challenges and high expectations. AI and machine learning models are pivotal in modern fraud detection strategies, analyzing historical data to establish patterns of normal behavior and flagging anomalies indicative of fraudulent activity. These models continuously learn and adapt to new data, enhancing their accuracy over time and making them indispensable tools in combating fraud.
Neural Technologies' advanced Fraud Management Solution (FMS) leverages AI and machine learning to compare enterprise data against historical trends, quickly identifying abnormal or high-risk behavior. This adaptive capability ensures that the system evolves to detect new types of fraud as they emerge.
Discover how our powerful FMS can secure your operations:
- Comprehensive Fraud Coverage: Integrates unlimited data sources with machine learning to identify both known and unknown fraud.
- Integrated Network Signaling Analysis: Maximizes detection speed, coverage, and accuracy while reducing losses through active/passive network integration.
- Fast, Accurate & Scalable: Employs multiple near real-time analysis tools, including link analysis, advanced rules, profiling, AI/ML structural analysis, and prediction.
- Customizable: Offers flexible setup and configuration to meet both present and future revenue protection needs.
In addition to FMS, our ActivML platform provides a comprehensive solution with predictive analytics capabilities. ActivML is a fully automated and self-learning system developed with adaptive insights beyond risk and fraud detection, not only identifies and mitigates risks but also provides deep, adaptive insights into potential vulnerabilities and emerging threats, ensuring comprehensive protection for your operations.
ActivML delves deep into network usage, payment patterns, and subscriber behaviors, utilizing data-driven and explainable AI (XAI) to identify inconsistencies and suspicious trends. The solution's effectiveness is further enhanced by built-in MLOps support, ensuring continuous model evolution for both current and future fraud challenges.
