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 |
|---|---|
| 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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