Method of Credit Fraud Detection by Combining Sub-graph Selection and Neighborhood Filtering
Credit fraud detection is a hot and difficult research topic in the field of financial fraud detection, especially fraud detection in the scenario of large-scale financial credit transactions. However, the extremely uneven distribution of fraudster nodes in the credit fraud review process and the pr...
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| Format: | Article |
| Language: | zho |
| Published: |
Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2025-02-01
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| Series: | Jisuanji kexue yu tansuo |
| Subjects: | |
| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2402040.pdf |
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| Summary: | Credit fraud detection is a hot and difficult research topic in the field of financial fraud detection, especially fraud detection in the scenario of large-scale financial credit transactions. However, the extremely uneven distribution of fraudster nodes in the credit fraud review process and the problem of fraudster nodes disguising themselves have always been important challenges. Therefore, researchers propose a graph neural network model that integrates reconstruction subgraph selection and reinforced neighborhood filtering (RSRF-GNN) for large-scale Internet financial credit dynamic graphs. In order to improve the effectiveness of credit fraud audit, this method first defines the unbalanced distribution of the number of fraudsters and fraud camouflage problem from the data perspective. Then, according to the node category and access degree information design, the balance subgraph selection module is reconstructed to solve the unbalanced distribution of the number of fraudsters. Next, for the fraud camouflage problem, researchers introduce the reinforcement learning framework and design a neighborhood filtering module embedded in dynamic filtering neighborhood nodes. In addition, researchers design an edge aggregation module to aggregate the neighborhood edge embedding of central nodes, further enriching the expression of neighborhood embedding information of central nodes. Finally, experimental verification is conducted on a real dataset DGraph-Fin, and the results show that the RSRF-GNN model proposed in this paper has significantly improved the effectiveness compared with existing models. The RSRF-GNN model is improved by 5 to 8 percentage points in AUC and 18 to 29 percentage points in AP score, which is a significant advantage in model performance. |
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| ISSN: | 1673-9418 |