A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914224750460928 |
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| author | Hewageegana, Kanishka Harischandra, Janani Senanayake, Nipuna Danansuriya, Gihan Hapuarachchi, Kavindu Illangarathne, Pooja |
| author_facet | Hewageegana, Kanishka Harischandra, Janani Senanayake, Nipuna Danansuriya, Gihan Hapuarachchi, Kavindu Illangarathne, Pooja |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23777 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms Hewageegana, Kanishka Harischandra, Janani Senanayake, Nipuna Danansuriya, Gihan Hapuarachchi, Kavindu Illangarathne, Pooja Machine Learning Artificial Intelligence 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. |
| title | A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.23777 |