A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms

TL;DR

This survey reviews the application of Graph Neural Networks in detecting fraud within ride-hailing platforms, emphasizing current methodologies, challenges like class imbalance, and future research directions for practical deployment.

Contribution

It provides a comprehensive overview of GNN architectures and techniques used for fraud detection in ride-hailing, highlighting gaps and proposing areas for future research.

Findings

GNNs are effective in modeling complex fraud patterns.

Addressing class imbalance remains a key challenge.

Methodological gaps hinder real-world application.

Abstract

This study investigates fraud detection in ride hailing platforms through Graph Neural Networks (GNNs),focusing on the effectiveness of various models. By analyzing prevalent fraudulent activities, the research highlights and compares the existing work related to fraud detection which can be useful when addressing fraudulent incidents within the online ride hailing platforms. Also, the paper highlights addressing class imbalance and fraudulent camouflage. It also outlines a structured overview of GNN architectures and methodologies applied to anomaly detection, identifying significant methodological progress and gaps. The paper calls for further exploration into real-world applicability and technical improvements to enhance fraud detection strategies in the rapidly evolving ride-hailing industry.