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

arXiv: https://arxiv.org/abs/2512.23777 · HTML: https://mlsanity.com/paper/2512.23777/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 53 references that are in the map.
- First posted on arXiv 29 Dec 2025.

## Builds on (most cited first, 26 of 26)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Hyperbolic Graph Convolutional Neural Networks](https://mlsanity.com/paper/1910.12933/) (2019)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks](https://mlsanity.com/paper/2002.03665/) (2020)
- [Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection](https://mlsanity.com/paper/2005.00625/) (2020)
- [Financial Fraud Detection using Quantum Graph Neural Networks](https://mlsanity.com/paper/2309.01127/) (2023)
- [SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs](https://mlsanity.com/paper/2305.13573/) (2023)
- [Semi-supervised Anomaly Detection on Attributed Graphs](https://mlsanity.com/paper/2002.12011/) (2020)
- [Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes](https://mlsanity.com/paper/2112.10558/) (2021)
- [Graph convolutional networks for learning with few clean and many noisy labels](https://mlsanity.com/paper/1910.00324/) (2019)
- [Graph Anomaly Detection with Unsupervised GNNs](https://mlsanity.com/paper/2210.09535/) (2022)
- [Spade: A Real-Time Fraud Detection Framework on Evolving Graphs (Complete Version)](https://mlsanity.com/paper/2211.06977/) (2022)
- [Improving Fraud Detection via Hierarchical Attention-based Graph Neural Network](https://mlsanity.com/paper/2202.06096/) (2022)
- [Modelling graph dynamics in fraud detection with "Attention"](https://mlsanity.com/paper/2204.10614/) (2022)
- [Deep Fraud Detection on Non-attributed Graph](https://mlsanity.com/paper/2110.01171/) (2021)
- [GADY: Unsupervised Anomaly Detection on Dynamic Graphs](https://mlsanity.com/paper/2310.16376/) (2023)
- [How effective are Graph Neural Networks in Fraud Detection for Network Data?](https://mlsanity.com/paper/2105.14568/) (2021)
- [Adaptive Fraud Detection System Using Dynamic Risk Features](https://mlsanity.com/paper/1810.04654/) (2018)
- [PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels](https://mlsanity.com/paper/2211.08604/) (2022)
- [DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Risk](https://mlsanity.com/paper/2303.03933/) (2023)
- [BeeTLe: An Imbalance-Aware Deep Sequence Model for Linear B-Cell Epitope Prediction and Classification with Logit-Adjusted Losses](https://mlsanity.com/paper/2309.02071/) (2023)

## Abstract (arXiv)

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.