Traffic pumping is a type of fraud committed in several countries, in which small telephone operators inflate the number of incoming calls to their networks, profiting from a higher access charge in relation to the network operator associated with the origin of the call. The identification of traffic pumping is complex due to the lack of labels for performing supervised learning, and the scarce literature on the topic. We propose a decision support system for fraud detection via clustering and decision trees. After data collection and feature engineering, we group the potential fraud cases into various clusters via an unsupervised learning approach. Then, we constructed a decision tree by using the cluster memberships as labels, evolving into the rules of a given variable and a certain label required for filing lawsuits against the suspicious cases. Telecommunication experts validate these rules to seek a legal resource against alleged perpetrators. We present the results of a case study from a Chilean telecommunication provider. All the lawsuits taken by the legal department were granted, confirming our success in dramatically reducing current and future fraud losses for the company.

流量抽取是在几个国家实施的一种欺诈行为，其中小型电话运营商夸大其网络的来电数量，从与呼叫来源相关的网络运营商的更高接入费中获利。由于缺乏用于执行监督学习的标签，以及关于该主题的文献稀缺，交通泵的识别很复杂。我们提出了一种通过聚类和决策树进行欺诈检测的决策支持系统。在数据收集和特征工程之后，我们通过无监督学习方法将潜在的欺诈案例分组到不同的集群中。然后，我们使用集群成员作为标签构建决策树，演变为对可疑案件提起诉讼所需的给定变量和特定标签的规则。电信专家验证这些规则以寻求针对被指控的肇事者的法律资源。我们展示了来自智利电信提供商的案例研究结果。法务部提起的所有诉讼均获得批准，这证实了我们在大幅减少公司当前和未来欺诈损失方面取得的成功。