FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection
ICAIF ’24, November 14–17, 2024, Brooklyn, NY, USA
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM|5th ACM International Conference on AI in Finance
2024
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| Online Access: | https://hdl.handle.net/1721.1/157762 |
| _version_ | 1824458311595982848 |
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| author | Lin, Junhong Guo, Xiaojie Zhu, Yada Mitchell, Samuel Altman, Erik Shun, Julian |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Lin, Junhong Guo, Xiaojie Zhu, Yada Mitchell, Samuel Altman, Erik Shun, Julian |
| author_sort | Lin, Junhong |
| collection | MIT |
| description | ICAIF ’24, November 14–17, 2024, Brooklyn, NY, USA |
| first_indexed | 2025-02-19T04:23:53Z |
| format | Article |
| id | mit-1721.1/157762 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:23:53Z |
| publishDate | 2024 |
| publisher | ACM|5th ACM International Conference on AI in Finance |
| record_format | dspace |
| spelling | mit-1721.1/1577622025-01-05T04:32:16Z FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection Lin, Junhong Guo, Xiaojie Zhu, Yada Mitchell, Samuel Altman, Erik Shun, Julian Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science ICAIF ’24, November 14–17, 2024, Brooklyn, NY, USA Fraud detection plays a crucial role in the financial industry, preventing significant financial losses. Traditional rule-based systems and manual audits often struggle with the evolving nature of fraud schemes and the vast volume of transactions. Recent advances in machine learning, particularly graph neural networks (GNNs), have shown promise in addressing these challenges. However, GNNs still face limitations in learning intricate patterns, effectively utilizing edge attributes, and maintaining efficiency on large financial graphs. To address these limitations, we introduce FraudGT, a simple, effective, and efficient graph transformer (GT) model specifically designed for fraud detection in financial transaction graphs. FraudGT leverages edge-based message passing gates and an edge attribute-based attention bias to enhance its ability to discern important transactional features and differentiate between normal and fraudulent transactions. Our model achieves state-of-the-art performance in detecting fraudulent activities while demonstrating high throughput and significantly lower latency compared to existing methods. We validate the effectiveness of FraudGT through extensive experiments on multiple large-scale synthetic financial datasets. FraudGT consistently outperforms other models, achieving 7.8–17.8% higher F1 scores, while delivering an average of 2.4 × greater throughput and reduced latency. Our code and datasets are available at https://github.com/junhongmit/FraudGT. 2024-12-05T21:47:34Z 2024-12-05T21:47:34Z 2024-11-14 2024-12-01T08:47:09Z Article http://purl.org/eprint/type/ConferencePaper 979-8-4007-1081-0 https://hdl.handle.net/1721.1/157762 Lin, Junhong, Guo, Xiaojie, Zhu, Yada, Mitchell, Samuel, Altman, Erik et al. 2024. "FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection." PUBLISHER_CC en https://doi.org/10.1145/3677052.3698648 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The author(s) application/pdf ACM|5th ACM International Conference on AI in Finance Association for Computing Machinery |
| spellingShingle | Lin, Junhong Guo, Xiaojie Zhu, Yada Mitchell, Samuel Altman, Erik Shun, Julian FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection |
| title | FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection |
| title_full | FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection |
| title_fullStr | FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection |
| title_full_unstemmed | FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection |
| title_short | FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection |
| title_sort | fraudgt a simple effective and efficient graph transformer for financial fraud detection |
| url | https://hdl.handle.net/1721.1/157762 |
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