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Interconnected nodes representing the use of machine learning algorithms for fraud detection to tackle dealer fraud and abuse.
Neural Technologies3 min read

Fraud Detection with AI and Machine Learning in Telecom Dealers Network

Dealer Fraud in Telecommunications

Telecommunications, or telecom, has undergone a significant transformation in recent decades. In this dynamic and fiercely competitive market, telecom operators heavily rely on an expansive network of indirect dealer partners to broaden their market reach, enhance customer service, and optimize operational efficiency.

These indirect dealer networks encompass various third-party entities, such as dealers, agents, resellers, and retailers, and typically involve numerous point-of-sale outlets. These agents play crucial roles in selling devices, acquiring new customers, and managing existing customer relationships.

However, managing the administration of these indirect sales channels presents significant challenges. Unethical dealers may exploit or abuse partner agreements for personal gain, compromising legitimate sales efforts. 

The common forms of dealer abuse or fraudulent activities in the telecom industry include: 

  • Subscription fraud which involves false application submission or false subscription to create accounts to obtain services or devices with false information or fake identities, including stolen identities, hybrid Identities, and synthetic identities. 
  • Commission and incentive scheme abuse involves activities that exploit sales incentives, bonuses, or commissions offered by telecom companies to earn higher commissions or incentives which can include cases such as false account subscriptions, reactivation of old accounts (flipping), commission stacking, identity theft and more. 
  • Reselling of Stock which involves reselling telecom products and services in unauthorized regions or in ways not permitted by the telecom company.       
  • Package splitting where dealers take advantage of discounts or promotions by breaking device, service or accessories bundles and resell them individually at higher prices.  
  • SIM Card fraud which involves assisting in fraudulent SIM swaps or bulk SIM card purchases for activations. 

Challenges in Detecting Dealer Abuse and Fraudulent Activities 

Traditional methods of detecting dealer abuse and fraudulent activities often rely on manual audits and data analytics, which are inherently slow and inefficient in managing business operations. These methods involve sifting through vast amounts of transaction data, sales records, and commission reports. Such processes are not only time-consuming but also prone to human error, leading to missed fraudulent activities or false positives that can waste valuable resources. This inefficiency means that by the time suspicious activity is identified, the financial damage can be substantial.

Underlining the significance of this issue, the Communications Fraud Control Association’s (CFCA) 2023 report highlights annual losses of approximately $1.2 billion attributed to dealer and commission fraud in the telecommunications industry. 

Effectively addressing dealer abuse requires advanced, dynamic solutions capable of adapting to emerging abuse or /fraud patterns, including vigilant monitoring of dealer activities. Proactively tackling dealer fraud or abuse is crucial for protecting revenue and maintaining the credibility of telecom services. 

Leveraging AI models and machine learning technologies can provide real-time detection and prevention of dealer abuse and fraudulent activities. These technologies can analyze vast datasets quickly, identify complex fraud patterns, and adapt to new commission fraud or abuse tactics as they emerge. The urgency to adopt these technologies cannot be overstated, as the risks and costs associated with delayed action continue to escalate.

ActivML: Dealer Abuse/Fraud Detection with Advanced Data Analytics 

Neural Technologies’ ActivML solution leverages advanced AI and machine learning capabilities to provide real-time insights and proactive measures to safeguard against fraudulent activities. It is a powerful solution equipped with a comprehensive suite of advanced AI models combined with machine learning explainability analysis, specifically designed to address the unique complexities of telecommunications business operations. 

ActivML (AI and machine learning) solution key features include:

  • Business-Enabled Automated Model Building, Training and Deployment
  • Self-learning Structured Analytical Profiling
  • Unconstrained Anomaly Detection
  • Predictive Classification
  • Explainable AI Analytics (XAI)
  • Continuous Learning from Live Data

Here's the real-world example on how ActivML helps in tackling dealers abuse:

  • A Communication Service Provider (CSP) faced commission abuse and fraud challenges within its indirect sales channels, which accounted for 80% of their sales. Traditional methods struggled to detect the abuse and fraudulent activities rapidly.

Neural Technologies deployed the ActivML solution to address specific abuse or fraud challenges. The suspicious dealers’ activity and activated MSISDNs associated with the subscription fraud were successfully identified quickly. In contrast to the traditional detection method, which typically led to detection months later, ActivML's real-time detection capabilities ensure rapid identification and resolution of fraudulent activities.

Neural Technologies’ ActivML platform provides real-time data analytics and advanced anomaly detection capabilities to uncover hidden patterns of dealer abuse and other emerging fraud.

Explore how ActivML's advanced machine learning and AI models can combat dealer abuse and fraud effectively.