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August 21, 2025Open Access

Lightweight Financial Fraud Detection Using a Symmetrical GAN-CNN Fusion Architecture

YYYiwen YangChina University of Geosciences CXChengjun XuSouthwest University GTGuiyou TianGannan Normal University

Key Points

The proposed GAN-CNN model improves accuracy in detecting financial fraud patterns, enhancing recognition of complex cases.
It achieved a 1.64% accuracy improvement over the CNN-GAN model and reduced parameters by about 88.4% during testing.
Utilizing synthetic fraud samples generated by GAN helps address data imbalances in financial transaction datasets.
The model incorporates advanced mechanisms for extracting local and global features, indicating significant advancements in computational efficiency.

Abstract

With the rapid development of information technology and the deep integration of the Internet platform, the scale and form of financial transactions continue to grow and expand, significantly improving users’ payment experience and life efficiency. However, financial transactions bring us convenience but also expose many security risks, such as money laundering activities, forged checks, and other financial fraud that occurs frequently, seriously threatening the stability and security of the financial system. Due to the imbalance between the proportion of normal and abnormal transactions in the data, most of the existing deep learning-based methods still have obvious deficiencies in learning small numbers sample classes, context modeling, and computational complexity control. To address these deficiencies, this paper proposes a symmetrical structure-based GAN-CNN model for lightweight financial fraud detection. The symmetrical structure can improve the feature extraction and fusion ability and enhance the model’s recognition effect for complex fraud patterns. Synthetic fraud samples are generated based on a GAN to alleviate category imbalance. Multi-scale convolution and attention mechanisms are designed to extract local and global transaction features, and adaptive aggregation and context encoding modules are introduced to improve computational efficiency. We conducted numerous replicate experiments on two public datasets, YelpChi and Amazon. The results showed that on the Amazon dataset with a 50% training ratio, compared with the CNN-GAN model, the accuracy of our model was improved by 1.64%, and the number of parameters was reduced by approximately 88.4%. Compared with the hybrid CNN-LSTM–attention model under the same setting, the accuracy was improved by 0.70%, and the number of parameters was reduced by approximately 87.6%. The symmetry-based lightweight architecture proposed in this work is novel in terms of structural design, and the experimental results show that it is both efficient and accurate in detecting imbalanced transactions.

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