Anomaly Detection in Car-Booking Graphs.

The use of car-booking services has gained massive popularity in the recent years - which led to an increasing number of fraudsters that try to game these systems. In this paper we describe a framework for fraud detection in car-booking systems. Our core idea lies in casting this problem as an instance of anomaly detection in temporal graphs. Specifically, we use unsupervised techniques, such as dense subblock discovery, to detect suspicious activity. The proposed framework is able to adapt to the variations in the data inherent to the car-booking setting, and detects fraud with high precision. This work is performed in collaboration with Careem, where the described framework is currently being deployed in production.