A synthetic data set to benchmark anti-money laundering methods

Bank transactions are highly confidential. As a result, there are no real public data sets that can be used to investigate and compare anti-money laundering (AML) methods in banks. This severely limits research on important AML problems such as efficiency, effectiveness, class imbalance, concept dri...

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Autores principales: Jensen, Rasmus Ingemann Tuffveson, Ferwerda, Joras, Jørgensen, Kristian Sand, Jensen, Erik Rathje, Borg, Martin, Krogh, Morten Persson, Jensen, Jonas Brunholm, Iosifidis, Alexandros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Data Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539331/
https://www.ncbi.nlm.nih.gov/pubmed/37770445
http://dx.doi.org/10.1038/s41597-023-02569-2
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author Jensen, Rasmus Ingemann Tuffveson
Ferwerda, Joras
Jørgensen, Kristian Sand
Jensen, Erik Rathje
Borg, Martin
Krogh, Morten Persson
Jensen, Jonas Brunholm
Iosifidis, Alexandros
author_facet Jensen, Rasmus Ingemann Tuffveson
Ferwerda, Joras
Jørgensen, Kristian Sand
Jensen, Erik Rathje
Borg, Martin
Krogh, Morten Persson
Jensen, Jonas Brunholm
Iosifidis, Alexandros
author_sort Jensen, Rasmus Ingemann Tuffveson
collection PubMed
description Bank transactions are highly confidential. As a result, there are no real public data sets that can be used to investigate and compare anti-money laundering (AML) methods in banks. This severely limits research on important AML problems such as efficiency, effectiveness, class imbalance, concept drift, and interpretability. To address the issue, we present SynthAML: a synthetic data set to benchmark statistical and machine learning methods for AML. The data set builds on real data from Spar Nord, a systemically important Danish bank, and contains 20,000 AML alerts and over 16 million transactions. Experimental results indicate that performance on SynthAML can be transferred to the real world. As use cases, we present and discuss open problems in the AML literature.
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spelling pubmed-105393312023-09-30 A synthetic data set to benchmark anti-money laundering methods Jensen, Rasmus Ingemann Tuffveson Ferwerda, Joras Jørgensen, Kristian Sand Jensen, Erik Rathje Borg, Martin Krogh, Morten Persson Jensen, Jonas Brunholm Iosifidis, Alexandros Sci Data Data Descriptor Bank transactions are highly confidential. As a result, there are no real public data sets that can be used to investigate and compare anti-money laundering (AML) methods in banks. This severely limits research on important AML problems such as efficiency, effectiveness, class imbalance, concept drift, and interpretability. To address the issue, we present SynthAML: a synthetic data set to benchmark statistical and machine learning methods for AML. The data set builds on real data from Spar Nord, a systemically important Danish bank, and contains 20,000 AML alerts and over 16 million transactions. Experimental results indicate that performance on SynthAML can be transferred to the real world. As use cases, we present and discuss open problems in the AML literature. Nature Publishing Group UK 2023-09-28 /pmc/articles/PMC10539331/ /pubmed/37770445 http://dx.doi.org/10.1038/s41597-023-02569-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Jensen, Rasmus Ingemann Tuffveson
Ferwerda, Joras
Jørgensen, Kristian Sand
Jensen, Erik Rathje
Borg, Martin
Krogh, Morten Persson
Jensen, Jonas Brunholm
Iosifidis, Alexandros
A synthetic data set to benchmark anti-money laundering methods
title A synthetic data set to benchmark anti-money laundering methods
title_full A synthetic data set to benchmark anti-money laundering methods
title_fullStr A synthetic data set to benchmark anti-money laundering methods
title_full_unstemmed A synthetic data set to benchmark anti-money laundering methods
title_short A synthetic data set to benchmark anti-money laundering methods
title_sort synthetic data set to benchmark anti-money laundering methods
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539331/
https://www.ncbi.nlm.nih.gov/pubmed/37770445
http://dx.doi.org/10.1038/s41597-023-02569-2
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