FinGraphFL: Financial Graph-Based Federated Learning for Enhanced Credit Card Fraud Detection
In the field of credit card fraud detection, traditional methods often struggle due to their reliance on complex manual feature engineering or their inability to adapt to rapidly changing fraud patterns. This paper introduces an innovative approach called FinGraphFL, which merges graph-based learnin...
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MDPI AG
2025-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/9/1396 |
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| author | Zhenyu Xia Suvash C. Saha |
| author_facet | Zhenyu Xia Suvash C. Saha |
| author_sort | Zhenyu Xia |
| collection | DOAJ |
| description | In the field of credit card fraud detection, traditional methods often struggle due to their reliance on complex manual feature engineering or their inability to adapt to rapidly changing fraud patterns. This paper introduces an innovative approach called FinGraphFL, which merges graph-based learning with the principles of federated learning and improves security through differential privacy. FinGraphFL utilizes Graph Attention Networks to analyze dynamic relationships between daily credit card transaction records, enhancing its ability to detect fraudulent activities. With the addition of differential privacy, the model allows multiple financial institutions to collaborate to refine the detection model without sharing sensitive data, thus improving adaptability and accuracy. The results are tested in two public datasets that show that FinGraphFL achieves accuracy rates of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.9780</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.9839</mn></mrow></semantics></math></inline-formula>, significantly outperforming traditional methods. Building on these results, FinGraphFL sets the stage for future advances in real-time learning and global financial collaboration, ensuring simultaneous progress in security and privacy protections. |
| format | Article |
| id | doaj-art-e71fa7eb56da4cd0aae51ea27e7d75ad |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-e71fa7eb56da4cd0aae51ea27e7d75ad2025-08-20T02:24:47ZengMDPI AGMathematics2227-73902025-04-01139139610.3390/math13091396FinGraphFL: Financial Graph-Based Federated Learning for Enhanced Credit Card Fraud DetectionZhenyu Xia0Suvash C. Saha1School of Mechanical and Mechatronic Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, AustraliaSchool of Mechanical and Mechatronic Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, AustraliaIn the field of credit card fraud detection, traditional methods often struggle due to their reliance on complex manual feature engineering or their inability to adapt to rapidly changing fraud patterns. This paper introduces an innovative approach called FinGraphFL, which merges graph-based learning with the principles of federated learning and improves security through differential privacy. FinGraphFL utilizes Graph Attention Networks to analyze dynamic relationships between daily credit card transaction records, enhancing its ability to detect fraudulent activities. With the addition of differential privacy, the model allows multiple financial institutions to collaborate to refine the detection model without sharing sensitive data, thus improving adaptability and accuracy. The results are tested in two public datasets that show that FinGraphFL achieves accuracy rates of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.9780</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.9839</mn></mrow></semantics></math></inline-formula>, significantly outperforming traditional methods. Building on these results, FinGraphFL sets the stage for future advances in real-time learning and global financial collaboration, ensuring simultaneous progress in security and privacy protections.https://www.mdpi.com/2227-7390/13/9/1396privacy preservingfederated learninglocal federalizationdifferential privacy |
| spellingShingle | Zhenyu Xia Suvash C. Saha FinGraphFL: Financial Graph-Based Federated Learning for Enhanced Credit Card Fraud Detection Mathematics privacy preserving federated learning local federalization differential privacy |
| title | FinGraphFL: Financial Graph-Based Federated Learning for Enhanced Credit Card Fraud Detection |
| title_full | FinGraphFL: Financial Graph-Based Federated Learning for Enhanced Credit Card Fraud Detection |
| title_fullStr | FinGraphFL: Financial Graph-Based Federated Learning for Enhanced Credit Card Fraud Detection |
| title_full_unstemmed | FinGraphFL: Financial Graph-Based Federated Learning for Enhanced Credit Card Fraud Detection |
| title_short | FinGraphFL: Financial Graph-Based Federated Learning for Enhanced Credit Card Fraud Detection |
| title_sort | fingraphfl financial graph based federated learning for enhanced credit card fraud detection |
| topic | privacy preserving federated learning local federalization differential privacy |
| url | https://www.mdpi.com/2227-7390/13/9/1396 |
| work_keys_str_mv | AT zhenyuxia fingraphflfinancialgraphbasedfederatedlearningforenhancedcreditcardfrauddetection AT suvashcsaha fingraphflfinancialgraphbasedfederatedlearningforenhancedcreditcardfrauddetection |