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Financial Fraud Detection Based on Deep Learning: Towards Large-Scale Pre-training Transformer Models

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Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence (CCKS 2023)

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Abstract

Fraud detection is a critical issue in the field of finance, as it can help to prevent fraud and minimize losses caused by fraud. Deep learning techniques learn the intrinsic knowledge of huge data, build explainable transaction knowledge graphs, and effectively predict potential fraudulent transactions, making it an essential technique in financial fraud detection. In this paper, we systematically review the existing financial fraud detection technologies, focusing on deep learning-based financial fraud detection methods. To the best of our knowledge, our work is the first to systematically introduce financial fraud detection methods based on transformer models, including the most recent pre-training transformer models, which can be thought of as parametric knowledge. Finally, we also analyze and summarize the challenges of financial fraud detection research, to promote its future development of research.

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Acknowledgments

This work was supported by the National Key R &D Program of China (2021YFB2700500, 2021YFB2700501), the self-established Key Research Fund (119001-BB2201) of Intelligent Computing Theory and Method from Zhejiang Laboratory. The authors wish to acknowledge Dr. Fei Yu, Guilin Qi, and Yuandi Li for their contribution to the discussion and review. In addition, the authors wish to acknowledge the editor and anonymous reviewers for their insightful comments, which have improved the quality of this publication.

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Southeast University, Nanjing, 210096, China

    Haitao Wang

  2. Zhejiang Laboratory, Hangzhou, 311121, China

    Haitao Wang, Ivan E. Carvajal-Roca, Linghui Chen & Mengqiu Bai

  3. Huawei Noah’s Ark Lab, Shenzhen, 518129, China

    Jiale Zheng

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  1. Haitao Wang
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  2. Jiale Zheng
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  3. Ivan E. Carvajal-Roca
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  4. Linghui Chen
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  5. Mengqiu Bai
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Correspondence to Haitao Wang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    Haofen Wang

  2. Chinese Academy of Sciences, Beijing, China

    Xianpei Han

  3. Harbin Institute of Technology, Harbin, China

    Ming Liu

  4. Nanjing University, Nanjing, China

    Gong Cheng

  5. University of South China, Hengyang, China

    Yongbin Liu

  6. Zhejiang University, Hangzhou, China

    Ningyu Zhang

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Wang, H., Zheng, J., Carvajal-Roca, I.E., Chen, L., Bai, M. (2023). Financial Fraud Detection Based on Deep Learning: Towards Large-Scale Pre-training Transformer Models. In: Wang, H., Han, X., Liu, M., Cheng, G., Liu, Y., Zhang, N. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence. CCKS 2023. Communications in Computer and Information Science, vol 1923. Springer, Singapore. https://doi.org/10.1007/978-981-99-7224-1_13

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  • DOI: https://doi.org/10.1007/978-981-99-7224-1_13

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