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Improving Anti-money Laundering via Fourier-Based Contrastive Learning

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Advances in Knowledge Discovery and Data Mining (PAKDD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14647))

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Abstract

Anti-money laundering (AML) aims to detect money laundering from daily transactions, which is the key frontier of combating financial crimes. Previous deep-learning AML methods are not robust enough. To address the problem, we propose a novel Fourier-based contrastive learning model (FCLM) to improve AML. With contrastive learning, FCLM can maintain prediction consistency and be more robust in the face of data perturbations. Experiments on both the synthetic benchmark IBM2023 and the real-world benchmark show that FCLM outperforms seven state-of-the-art baselines, demonstrating the effectiveness of the proposed Fourier-based contrastive learning model.

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Notes

  1. 1.

    https://s3.ca-central-1.amazonaws.com/esentire-dot-com-assets/assets/resourcefiles/2022-Official-Cybercrime-Report.pdf.

  2. 2.

    https://www.kaggle.com/code/bigironsphere/basic-data-augmentation-feature-reduction.

  3. 3.

    https://www.kaggle.com/datasets/ealtman2019/ibm-transactions-for-anti-money-laundering-aml.

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Author information

Authors and Affiliations

  1. China National Clearing Center, Beijing, China

    Meihan Tong, Xinyu Chen & Jinsong Bei

  2. Tsinghua University, Beijing, China

    Meihan Tong

  3. Hashkey, Beijing, China

    Shuai Wang

Authors
  1. Meihan Tong
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  2. Shuai Wang
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  3. Xinyu Chen
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  4. Jinsong Bei
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Corresponding author

Correspondence to Meihan Tong .

Editor information

Editors and Affiliations

  1. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  4. Duke University, Durham, NC, USA

    Jian Pei

  5. National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  6. Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

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Tong, M., Wang, S., Chen, X., Bei, J. (2024). Improving Anti-money Laundering via Fourier-Based Contrastive Learning. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14647. Springer, Singapore. https://doi.org/10.1007/978-981-97-2259-4_25

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  • DOI: https://doi.org/10.1007/978-981-97-2259-4_25

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  • Online ISBN: 978-981-97-2259-4

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