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