Telecom Fraud Detection Using Machine Learning

Abstract: International Revenue Sharing Fraud (IRSF) is one of the most persistent types of fraud within the telecommunications industry. According to the 2017 Communications Fraud Control Association (CFCA) fraud loss survey, IRSF costs 6 billion dollars a year. Therefore, the detection of such frauds is of vital importance to avoid further loss. Though many efforts have been made, very few utilize the temporal patterns of phone call traffic. This project, supported with Sinch’s real production data, aims to exploit both spatial and temporal patterns learned by Graph Attention Neural network (GAT) with Gated Recurrent Unit (GRU) to find suspicious timestamps in the historical traffic. Moreover, combining with the time-independent Isolation forest model, our model should give better results for the phone call records. This report first explains the mechanism of IRSF in detail and introduces the models that are applied in this project, including GAT, GRU, and Isolation forest. Finally, it presents how our experiments have been conducted and the results with extensive analysis. Moreover, we have achieved 42.4% precision and 96.1% recall on the test data provided by Sinch, showing significant advantages over both previous work and baselines.