mlsanityData to 14 Oct 2026

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

arXiv 2512.23777 ↗First posted 29 Dec 2025cs.LG cs.AIcited by 0

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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.

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.

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References · 26

  1. 2017Graph Attention Networkscited by 8,923
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320
  3. 2018How Powerful are Graph Neural Networks?cited by 4,393
  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  6. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
  7. 2019Hyperbolic Graph Convolutional Neural Networkscited by 402
  8. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 250
  9. 2020AnomalyDAE: Dual autoencoder for anomaly detection on attributed networkscited by 82
  10. 2020Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detectioncited by 82
  11. 2023Financial Fraud Detection using Quantum Graph Neural Networkscited by 35
  12. 2023SAD: Semi-Supervised Anomaly Detection on Dynamic Graphscited by 28
14 more
  1. 2020Semi-supervised Anomaly Detection on Attributed Graphscited by 18
  2. 2021Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classescited by 11
  3. 2019Graph convolutional networks for learning with few clean and many noisy labelscited by 8
  4. 2022Graph Anomaly Detection with Unsupervised GNNscited by 8
  5. 2022Spade: A Real-Time Fraud Detection Framework on Evolving Graphs (Complete Version)cited by 6
  6. 2022Improving Fraud Detection via Hierarchical Attention-based Graph Neural Networkcited by 5
  7. 2022Modelling graph dynamics in fraud detection with "Attention"cited by 5
  8. 2021Deep Fraud Detection on Non-attributed Graphcited by 3
  9. 2023GADY: Unsupervised Anomaly Detection on Dynamic Graphscited by 3
  10. 2021How effective are Graph Neural Networks in Fraud Detection for Network Data?cited by 2
  11. 2018Adaptive Fraud Detection System Using Dynamic Risk Featurescited by 1
  12. 2022PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labelscited by 1
  13. 2023DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Riskcited by 1
  14. 2023BeeTLe: An Imbalance-Aware Deep Sequence Model for Linear B-Cell Epitope Prediction and Classification with Logit-Adjusted Lossescited by 1

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