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Fighting Against Money Laundering: A Systematic Mapping

  • Bruno Luiz Kreutz Barroso
  • Fábio Mangueira
  • Methanias Colaço Júnior
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 800)

Abstract

Context: Money Laundering (ML) is a global crime that has a close relation with other crimes, such as: illegal drug trading, terrorism or arms trafficking. Criminals in today’s technology-driven society use every means available at their disposal to launder the profit made from their illegal activities. In response, international anti-money laundering (AML) efforts are made with AML systems. Objective: Identify and systematize the approaches, techniques and algorithms used in Computer Science (CS) to fight ML, besides identifying the trends in the field. Method: A systematic literature mapping was conducted to analyze the scientific research in the field. Results: The main approaches were identified, supervised classifiers and clusters, along with the trend of papers published over the years. China was the country with the highest number of published papers. Conclusion: The most relevant studies in such research line adopt data mining and machine learning techniques using clusters and classifiers. The state of the art was mapped, making it clear that it is an area of interest for researchers around the world with growth potential. We believe that this work is relevant to the academy, governments and the community at large, presenting them with trends in the detection of money laundering.

Keywords

Money laundering Data mining Artificial intelligence Machine learning Cyber crimes and profiling 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bruno Luiz Kreutz Barroso
    • 1
  • Fábio Mangueira
    • 1
  • Methanias Colaço Júnior
    • 1
  1. 1.Postgraduate Program in Computer Science – PROCCFederal University of Sergipe (UFS)São CristóvãoBrazil

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