Fraud Detection System in Online Ride-Hailing Services

Abstract

Advances in technology and the human tendency to use virtual services are constantly increasing in all areas of life. Online ride-hailing services are not an exception to this rule. Due to the financial transactions in these systems, the possibility of fraud by profiteers also increases which can affect the revenue of such services significantly. In this paper, we propose a system that can detect fraud in online ride-hailing systems. We address frauds that occur using the ride collusion method or creating a fake ride using GPS spoofing applications. We have used real unlabeled data from one of the largest ride-hailing companies in Iran for this purpose. Our system first identifies the most important features that help us distinguish real rides from fake rides, then it uses unsupervised learning methods to detect ride anomalies. After identifying the anomalies and examining these rides, we label the data, and use supervised learning methods to construct the fraud detection model.