Article

Applying data mining in investigating money laundering crimes

Authors:

Zhongfei (Mark) Zhang,

John J. Salerno,

Philip S. YuAuthors Info & Claims

KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 747 - 752

https://doi.org/10.1145/956750.956851

Published: 24 August 2003 Publication History

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Abstract

In this paper, we study the problem of applying data mining to facilitate the investigation of money laundering crimes (MLCs). We have identified a new paradigm of problems --- that of automatic community generation based on uni-party data, the data in which there is no direct or explicit link information available. Consequently, we have proposed a new methodology for Link Discovery based on Correlation Analysis (LDCA). We have used MLC group model generation as an exemplary application of this problem paradigm, and have focused on this application to develop a specific method of automatic MLC group model generation based on timeline analysis using the LDCA methodology, called CORAL. A prototype of CORAL method has been implemented, and preliminary testing and evaluations based on a real MLC case data are reported. The contributions of this work are: (1) identification of the uni-party data community generation problem paradigm, (2) proposal of a new methodology LDCA to solve for problems in this paradigm, (3) formulation of the MLC group model generation problem as an example of this paradigm, (4) application of the LDCA methodology in developing a specific solution (CORAL) to the MLC group model generation problem, and (5) development, evaluation, and testing of the CORAL prototype in a real MLC case data.

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Mohamed BFattah K(2024)A Comprehensive Survey on the Role of Law in Different Applications in Computer ScienceSocial Sciences10.11648/j.ss.20241305.1713:5(192-196)Online publication date: 10-Oct-2024
https://doi.org/10.11648/j.ss.20241305.17
Wang SWang PWu BZhu YLuo WPan Y(2024)Structural entropy minimization combining graph representation for money laundering identificationInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02129-z15:9(3951-3968)Online publication date: 10-Apr-2024
https://doi.org/10.1007/s13042-024-02129-z
Baccouri MTalbi DHakimi A(2024)The effects of auditing and reporting standards and country‐level governance on money laundering: A cross‐country analysisJournal of Public Affairs10.1002/pa.293524:3Online publication date: Jul-2024
https://doi.org/10.1002/pa.2935
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Index Terms

Applying data mining in investigating money laundering crimes
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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Published In

KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2003

736 pages

ISBN:1581137370

DOI:10.1145/956750

Conference Chair:
Lise Getoor
University of Maryland, College Park
,
General Chair:
Ted Senator
DARPA
,
Program Chairs:
Pedro Domingos
University of Washington
,
Christos Faloutsos
Carnegie Mellon University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2003

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KDD03

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KDD03: The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2003

Washington, D.C.

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KDD '03 Paper Acceptance Rate 46 of 298 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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Mohamed BFattah K(2024)A Comprehensive Survey on the Role of Law in Different Applications in Computer ScienceSocial Sciences10.11648/j.ss.20241305.1713:5(192-196)Online publication date: 10-Oct-2024
https://doi.org/10.11648/j.ss.20241305.17
Wang SWang PWu BZhu YLuo WPan Y(2024)Structural entropy minimization combining graph representation for money laundering identificationInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02129-z15:9(3951-3968)Online publication date: 10-Apr-2024
https://doi.org/10.1007/s13042-024-02129-z
Baccouri MTalbi DHakimi A(2024)The effects of auditing and reporting standards and country‐level governance on money laundering: A cross‐country analysisJournal of Public Affairs10.1002/pa.293524:3Online publication date: Jul-2024
https://doi.org/10.1002/pa.2935
Alexandre CBalsa J(2023)Incorporating machine learning and a risk-based strategy in an anti-money laundering multiagent systemExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119500217:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.119500
Kaur G(2023)Trust the Machine and Embrace Artificial Intelligence (AI) to Combat Money Laundering ActivitiesComputational Intelligence for Modern Business Systems10.1007/978-981-99-5354-7_4(63-81)Online publication date: 4-Nov-2023
https://doi.org/10.1007/978-981-99-5354-7_4
Nosrati MCai Y(2022)Verifying the Correctness of Analytic Query ResultsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.303731334:9(4527-4537)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TKDE.2020.3037313
Majumder ANath I(2021)A Study on Various Applications of Data Mining and Supervised Learning Techniques in Business Fraud DetectionMachine Learning Applications for Accounting Disclosure and Fraud Detection10.4018/978-1-7998-4805-9.ch008(108-125)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-4805-9.ch008
Chen ZSoliman WNazir AShorfuzzaman M(2021)Variational Autoencoders and Wasserstein Generative Adversarial Networks for Improving the Anti-Money Laundering ProcessIEEE Access10.1109/ACCESS.2021.30863599(83762-83785)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3086359
Kute DPradhan BShukla NAlamri A(2021)Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical ReviewIEEE Access10.1109/ACCESS.2021.30862309(82300-82317)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3086230
Teichmann FFalker M(2020)Money laundering through exchange officesJournal of Money Laundering Control10.1108/JMLC-07-2019-005926:3(445-461)Online publication date: 20-Jan-2020
https://doi.org/10.1108/JMLC-07-2019-0059
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