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

Harold Guéneau (), Alain Celisse () and Pascal Delange
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Harold Guéneau: SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) - UP1 - Université Paris 1 Panthéon-Sorbonne
Alain Celisse: LPP - Laboratoire Paul Painlevé - UMR 8524 - Université de Lille - CNRS - Centre National de la Recherche Scientifique, MODAL - MOdel for Data Analysis and Learning - LPP - Laboratoire Paul Painlevé - UMR 8524 - Université de Lille - CNRS - Centre National de la Recherche Scientifique - Université de Lille, Sciences et Technologies - Centre Inria de l'Université de Lille - Inria - Institut National de Recherche en Informatique et en Automatique - METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694 - Université de Lille - CHRU Lille - Centre Hospitalier Régional Universitaire [CHU Lille] - Polytech Lille - École polytechnique universitaire de Lille
Pascal Delange: Marble

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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.

Keywords: Deep learning; Machine learning; Transformer; Money laundering; Representation learning; Artificial Inteligence; Statistics; Contrastive learning (search for similar items in EconPapers)
Date: 2025-07-04
Note: View the original document on HAL open archive server: https://hal.science/hal-05140202v1
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