A graph-powered large-scale fraud detection system
Article
Li, Zhao, Wang, Biao, Huang, Jiaming, Jin, Yilun, Xu, Zenghui, Zhang, Ji and Gao, Jianliang. 2024. "A graph-powered large-scale fraud detection system." International Journal of Machine Learning and Cybernetics. 15 (1), p. 115–128. https://doi.org/10.1007/s13042-023-01786-w
Article Title | A graph-powered large-scale fraud detection system |
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ERA Journal ID | 125217 |
Article Category | Article |
Authors | Li, Zhao, Wang, Biao, Huang, Jiaming, Jin, Yilun, Xu, Zenghui, Zhang, Ji and Gao, Jianliang |
Journal Title | International Journal of Machine Learning and Cybernetics |
Journal Citation | 15 (1), p. 115–128 |
Number of Pages | 14 |
Year | 2024 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1868-8071 |
1868-808X | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s13042-023-01786-w |
Web Address (URL) | https://link.springer.com/article/10.1007/s13042-023-01786-w |
Abstract | Graph-powered fraud detection is a common issue in various areas, such as e-commerce, banking, insurance and social networks, where data can be naturally formulated as graph structure. Especially in e-commerce, due to its large scale and enormous amount of real-time transactions over millions of merchandises, fraud detection has become an important and serious problem. The challenges lie in three aspects: sparse fraud samples, complex features in online transactions and extra-large scale of e-commerce data. To deal with above issues, in this paper, we propose an efficient graph-powered large-scale fraud detection framework. Concretely, we first present a heterogeneous label propagation algorithm to recall more potentially fraudulent samples for further model training; then, we design a novel multi-view heterogeneous graph neural network model to obtain more accurate fraud predictions; finally, a fraud pattern analysis approach is presented to discover hidden fraud groups. In addition, in order to improve the efficiency and scalability of our proposed fraud detection framework, we present a large-scale fraud detection system deployed on a general graph computing engine. We conduct experiments on two real-world datasets. Results show that the proposed graph-powered fraud detection framework achieves high accuracy and superior scalability on large-scale graph data. |
Keywords | Fraud detection; E-commerce; Scalable computing; Label propagation; Graph neural network |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
460306. Image processing | |
460599. Data management and data science not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Alibaba-Zhejiang University Joint Institute of Frontier Technologies, China |
Zhejiang Lab, China | |
Link2do Technology, China | |
Southeast University, China | |
School of Mathematics, Physics and Computing | |
Central South University, China |
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https://research.usq.edu.au/item/z2631/a-graph-powered-large-scale-fraud-detection-system
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