research-article

Fighting organized crime by automatically detecting money laundering-related financial transactions

Authors:

Soujanya Nemalikanti,

Max M�hlh�userAuthors Info & Claims

ARES '21: Proceedings of the 16th International Conference on Availability, Reliability and Security

Article No.: 38, Pages 1 - 10

https://doi.org/10.1145/3465481.3469196

Published: 17 August 2021 Publication History

Abstract

Money laundering is the set of operations aimed at giving a legitimate appearance to capital whose origin is illegal, thus making it more difficult to identify and subsequently recover it. It is one of the phenomena on which the so-called underground economy relies and therefore constitutes a crime for which the charge for money laundering applies. For supporting the fight against this phenomenon, the interest towards analysis models for Anti-Money Laundering (AML) based on a combined use of automatic tools and artificial intelligence (AI) techniques increases, as it is also shown by the European Central Bank (ECB) during recent press conferences. Following this direction, this paper proposes a model for enhancing the detection of suspicious transactions related to money laundering. It is based on a set of features that are defined by considering different aspects such as the time, the amount of money, number of transactions, type of operations and level of internationalization. An AI-based computational approach centered on Machine Learning (ML) techniques has been adopted to evaluate the goodness of such feature-based model, in supporting the automatic detection of suspicious transactions, by experimenting 5 different classifiers. From the experiments emerged that the Random Forest provided the best performance not only among the classifiers tested within the paper, but also in comparison to those presented in the related work with an accuracy, a recall and f1-score greater than 94% by decreasing the False Positive Rate (FPR). Furthermore, an analysis on the feature importance has been provided, to understand which feature, among the proposed ones, plays the major role in such application domain.

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Cited By

Tong MWang SChen XBei J(2024)Improving Anti-money Laundering via�Fourier-Based Contrastive LearningAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2259-4_25(331-343)Online publication date: 25-Apr-2024
https://doi.org/10.1007/978-981-97-2259-4_25
Oztas BCetinkaya DAdedoyin FBudka MDogan HAksu G(2023)Enhancing Anti-Money Laundering: Development of a Synthetic Transaction Monitoring Dataset2023 IEEE International Conference on e-Business Engineering (ICEBE)10.1109/ICEBE59045.2023.00028(47-54)Online publication date: 4-Nov-2023
https://doi.org/10.1109/ICEBE59045.2023.00028
Vosyliute IMaknickiene N(2022)INVESTIGATION OF FINANCIAL FRAUD DETECTION BY USING COMPUTATIONAL INTELLIGENCE12th International Scientific Conference “Business and Management 2022”10.3846/bm.2022.787Online publication date: 2022
https://doi.org/10.3846/bm.2022.787

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ARES '21: Proceedings of the 16th International Conference on Availability, Reliability and Security

August 2021

1447 pages

ISBN:9781450390514

DOI:10.1145/3465481

Copyright � 2021 ACM.

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Published: 17 August 2021

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ARES 2021: The 16th International Conference on Availability, Reliability and Security

August 17 - 20, 2021

Vienna, Austria

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Cited By

Tong MWang SChen XBei J(2024)Improving Anti-money Laundering via Fourier-Based Contrastive LearningAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2259-4_25(331-343)Online publication date: 25-Apr-2024
https://doi.org/10.1007/978-981-97-2259-4_25
Oztas BCetinkaya DAdedoyin FBudka MDogan HAksu G(2023)Enhancing Anti-Money Laundering: Development of a Synthetic Transaction Monitoring Dataset2023 IEEE International Conference on e-Business Engineering (ICEBE)10.1109/ICEBE59045.2023.00028(47-54)Online publication date: 4-Nov-2023
https://doi.org/10.1109/ICEBE59045.2023.00028
Vosyliute IMaknickiene N(2022)INVESTIGATION OF FINANCIAL FRAUD DETECTION BY USING COMPUTATIONAL INTELLIGENCE12th International Scientific Conference “Business and Management 2022”10.3846/bm.2022.787Online publication date: 2022
https://doi.org/10.3846/bm.2022.787

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