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Representation learning with a transformer by contrastive learning for money laundering detection

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Listed:
  • Harold Gu'eneau

    (SAMM)

  • Alain Celisse

    (LPP, MODAL)

  • Pascal Delange

Abstract

The present work tackles the money laundering detection problem. A new procedure is introduced which exploits structured time series of both qualitative and quantitative data by means of a transformer neural network. The first step of this procedure aims at learning representations of time series through contrastive learning (without any labels). The second step leverages these representations to generate a money laundering scoring of all observations. A two-thresholds approach is then introduced, which ensures a controlled false-positive rate by means of the Benjamini-Hochberg (BH) procedure. Experiments confirm that the transformer is able to produce general representations that succeed in exploiting money laundering patterns with minimal supervision from domain experts. It also illustrates the higher ability of the new procedure for detecting nonfraudsters as well as fraudsters, while keeping the false positive rate under control. This greatly contrasts with rule-based procedures or the ones based on LSTM architectures.

Suggested Citation

  • Harold Gu'eneau & Alain Celisse & Pascal Delange, 2025. "Representation learning with a transformer by contrastive learning for money laundering detection," Papers 2507.08835, arXiv.org.
    • Handle: RePEc:arx:papers:2507.08835
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    File URL: http://arxiv.org/pdf/2507.08835
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    References listed on IDEAS

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    1. Lannoo, Karel & Parlour, Richard, 2021. "Anti-Money Laundering in the EU: Time to get serious," ECRI Papers 31980, Centre for European Policy Studies.
    2. Damian Kisiel & Denise Gorse, 2022. "Portfolio Transformer for Attention-Based Asset Allocation," Papers 2206.03246, arXiv.org.
    3. Roy Majed Sinno & Graham Baldock & Kimberly Gleason, 2023. "The evolution of trade-based money laundering schemes: a regulatory dialectic perspective," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 30(5), pages 1279-1290, February.
    4. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
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