Improving Anti-money Laundering via Fourier-Based Contrastive Learning

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.