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Financial Fraud Transaction Prediction Approach Based on Global Enhanced GCN and Bidirectional LSTM

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  • Yimo Chen

    (Wenzhou Vocational College of Science and Technology)

  • Mengyi Du

    (Lishui Vocational and Technical College)

Abstract

Money laundering is an act taken by criminals to cover up the nature and source of illegal gains. As money laundering data shows a complex time dependence, there is also a complex spatial correlation between different transactions. For this reason, we propose a financial fraud transaction prediction method based on global enhanced graph convolution and Bidirectional LSTM, called GEGCN-BiLSTM. First, BiLSTM is used to capture the time dependence in money laundering transactions. It not only considers the previous historical data, but also considers the information of subsequent time steps. Then, GEGCN is used to further mine the spatial global context relevance between different transactions. On each time stamp, the output information of GEGCN will be used as the input of BiLSTM to integrate time dependence and spatial dependence. The experimental results show that GEGCN-BiLSTM outperforms other comparison algorithms in terms of effectiveness and significance, providing a powerful tool for market transaction supervision.

Suggested Citation

  • Yimo Chen & Mengyi Du, 2025. "Financial Fraud Transaction Prediction Approach Based on Global Enhanced GCN and Bidirectional LSTM," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 1747-1766, August.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:2:d:10.1007_s10614-024-10791-2
    DOI: 10.1007/s10614-024-10791-2
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    References listed on IDEAS

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    1. Raffaella Barone & Donato Masciandaro, 2019. "Cryptocurrency or usury? Crime and alternative money laundering techniques," European Journal of Law and Economics, Springer, vol. 47(2), pages 233-254, April.
    2. Zhang, Zhendong & Ye, Lei & Qin, Hui & Liu, Yongqi & Wang, Chao & Yu, Xiang & Yin, Xingli & Li, Jie, 2019. "Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression," Applied Energy, Elsevier, vol. 247(C), pages 270-284.
    3. Singh, Kishore & Best, Peter, 2019. "Anti-Money Laundering: Using data visualization to identify suspicious activity," International Journal of Accounting Information Systems, Elsevier, vol. 34(C), pages 1-1.
    4. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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