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<p>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 have demonstrated some success in detecting suspicious financial activities, but they often fall short in terms of robustness and reliability. 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.</p>

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Enhancing anti-money laundering via Fourier-based contrastive learning

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Meihan Tong,
  • Shuai Wang

摘要

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 have demonstrated some success in detecting suspicious financial activities, but they often fall short in terms of robustness and reliability. 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.