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Integrating Client Profiling in an Anti-money Laundering Multi-agent Based System

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New Advances in Information Systems and Technologies

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 444))

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Abstract

Continuing previous work by the authors, where an Anti-Money Laundering (AML) agent-based system was introduced, we now provide some detail on one of the elements of this system—the learning component. The system we are developing focuses on how a financial institution, a bank, can obtain better results in AML initiatives. More specifically, we’re trying to improve the suspicious transaction signaling process and the subsequent final decision. For this, it is critical to model client behavior, having a clear definition of the different client profiles. Having available a real world data set of bank transactions, we explain in this contribution how some data-mining techniques were used in order to build the needed client profiles, and how the results obtained can be integrated in the system.

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Authors and Affiliations

  1. Faculdade de Ciências da Universidade de Lisboa, BioISI-MAS, Lisbon, Portugal

    Claudio Alexandre & João Balsa

Authors
  1. Claudio Alexandre
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  2. João Balsa
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Corresponding author

Correspondence to Claudio Alexandre .

Editor information

Editors and Affiliations

  1. DEI/FCT, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. ISEGI,, Universidade Nova de Lisboa, Lisboa, Portugal

    Ana Maria Correia

  3. College of Engineering, The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

  4. DSI, Universidade do Minho, Guimarães, Portugal

    Luis Paulo Reis

  5. Rua Dom Manoel de Medeiros, s/n, DoisIrm, Universidade Federal Rural de Pernambuco, Recife, Brazil

    Marcelo Mendonça Teixeira

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Alexandre, C., Balsa, J. (2016). Integrating Client Profiling in an Anti-money Laundering Multi-agent Based System. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M. (eds) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-31232-3_88

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  • DOI: https://doi.org/10.1007/978-3-319-31232-3_88

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  • Print ISBN: 978-3-319-31231-6

  • Online ISBN: 978-3-319-31232-3

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