Shi, Xiangting, Wang, Xiaochen, Zhang, Yakang, Zhang, Xiaoyi, Yu, Manning and Zhang, Lihao (2025) Innovative novel regularized memory graph attention capsule network for financial fraud detection. PLOS ONE, 20 (5). ISSN 1932-6203
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Abstract
Financial fraud detection (FFD) is crucial for ensuring the safety and efficiency of financial transactions. This article presents the Regularised Memory Graph Attention Capsule Network (RMGACNet), an original architecture aiming at improving fraud detection using Bidirectional Long Short-Term Memory (BiLSTM) networks combined with advanced feature extraction and classification algorithms. The model is tested on two reliable datasets: the European Cardholder (ECH) transactions dataset, which contains 284,807 transactions and 492 fraud instances, and the IEEE-CIS dataset, which has more than 1 million transactions. Our approach enhances comparison to existing methods of feature selection and classification accuracy. On the ECH dataset, RMGACNet achieves an accuracy of 0.9772, a precision of 0.9768, and an F1 score of 0.9770 measures; on the IEEE-CIS dataset, it achieves an accuracy of 0.9882, a precision of 0.9876 and an F1 score of 0.9879. The findings indicate that RMGACNet routinely surpasses existing models’ efficiency and accuracy while ensuring strong execution time performance, especially when handling large-scale datasets. The suggested model demonstrates scalability and stability, making it suitable for real-time financial systems.
Item Type: | Article |
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Additional Information: | © 2025 The Authors |
Divisions: | Finance |
Subjects: | H Social Sciences > HG Finance |
JEL classification: | C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure |
Date Deposited: | 29 May 2025 15:12 |
Last Modified: | 05 Jun 2025 09:24 |
URI: | http://eprints.lse.ac.uk/id/eprint/128224 |
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