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Erschienen in:
Neural Additive and Basis Models with Feature Selection and Interactions Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Improving Anti-money Laundering via Fourier-Based Contrastive Learning

verfasst von : Meihan Tong, Shuai Wang, Xinyu Chen, Jinsong Bei

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer Nature Singapore

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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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Vorheriges Kapitel SAWTab: Smoothed Adaptive Weighting for Tabular Data in Semi-supervised Learning
Nächstes Kapitel A Novel SegNet Model for Crack Image Semantic Segmentation in Bridge Inspection
Fußnoten
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Metadaten
Titel
Improving Anti-money Laundering via Fourier-Based Contrastive Learning
verfasst von
Meihan Tong
Shuai Wang
Xinyu Chen
Jinsong Bei
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-61-7

Electronic ISBN: 978-981-9722-59-4

Copyright-Jahr: 2024

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

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