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HAMLET: A Transformer Based Approach for Money Laundering Detection

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Cyber Security, Cryptology, and Machine Learning (CSCML 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13914))

Abstract

Money laundering has damaging economic, security, and social consequences, fueling criminal activities like terrorism, human and drug trafficking. Recent technological advancements have increased the complexity of laundering operations, prompting financial institutions to use more advanced Anti Money Laundering (AML) techniques. In particular, machine learning-based transaction monitoring can complement traditional rule-based systems, lowering the number of false positives and the need to manually review fraud alerts. In this paper, we present HAMLET, a scalable Deep Learning model for analyzing financial transactions and detecting money laundering patterns. HAMLET employs a hierarchical transformer enforcing an attention mechanism at the transaction and sequence level. By combining the two different levels, HAMLET can identify complex money laundering operations carried out through subsequent transactions. We experimentally evaluate HAMLET on a synthetic dataset simulating clients trading on international capital markets, showing that HAMLET outperforms state-of-the-art solutions in detecting fraudulent transactions. In the experiments, HAMLET achieves \(99\%\) precision in a binary classification scenario and up to \(95\%\) in a multi-class scenario with 5 different money laundering schemes. Lastly, we investigate the proposed model’s interpretability through a proxy method.

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Authors and Affiliations

  1. DEIB, Politecnico di Milano, Milan, Italy

    Maria Paola Tatulli, Tommaso Paladini, Mario D’Onghia, Michele Carminati & Stefano Zanero

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  1. Maria Paola Tatulli
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  2. Tommaso Paladini
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  3. Mario D’Onghia
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  4. Michele Carminati
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  5. Stefano Zanero
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Corresponding authors

Correspondence to Maria Paola Tatulli , Tommaso Paladini , Mario D’Onghia , Michele Carminati or Stefano Zanero .

Editor information

Editors and Affiliations

  1. Ben-Gurion University of the Negev, Be’er Sheva, Israel

    Shlomi Dolev

  2. Ben-Gurion University of the Negev, Be’er Sheva, Israel

    Ehud Gudes

  3. Zama, Meythet, France

    Pascal Paillier

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Tatulli, M.P., Paladini, T., D’Onghia, M., Carminati, M., Zanero, S. (2023). HAMLET: A Transformer Based Approach for Money Laundering Detection. In: Dolev, S., Gudes, E., Paillier, P. (eds) Cyber Security, Cryptology, and Machine Learning. CSCML 2023. Lecture Notes in Computer Science, vol 13914. Springer, Cham. https://doi.org/10.1007/978-3-031-34671-2_17

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  • DOI: https://doi.org/10.1007/978-3-031-34671-2_17

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