Fraud Detection on Streaming Customer Behavior Data with Unsupervised Learning Methods

In today's telecom industry, fraud detection is a significant research problem. As part of this research, we propose a methodology to detect fraud cases using machine learning models with big data processing and analysis platforms based on customer data in telecommunications. The prototype implementation of the proposed methodology has been designed, developed, and applied to the usage data set of telecommunications company subscribers. We perform performance tests on the developed prototype application to understand how successful the proposed methodology is and its scalability. The obtained results demonstrate the usability of the proposed method in the telecommunications sector.