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

Kanishka Hewageegana, Janani Harischandra, Nipuna Senanayake, Gihan Danansuriya, Kavindu Hapuarachchi +1 more
12/29/2025
cs.LGcs.AI

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{hewageegana2025survey,
  title  = {A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms},
  author = {Kanishka Hewageegana and Janani Harischandra and Nipuna Senanayake and Gihan Danansuriya and Kavindu Hapuarachchi and Pooja Illangarathne},
  year   = {2025},
  eprint = {2512.23777},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.23777v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2512.23777.svg)](https://codeofpaper.com/paper/2512.23777)

Discussion