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Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective

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  • Canhoto, Ana Isabel

Abstract

Financial services organisations facilitate the movement of money worldwide, and keep records of their clients’ identity and financial behaviour. As such, they have been enlisted by governments worldwide to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development, corresponding to Goal 16 of the United Nations Sustainable Development Goals. In this paper, we investigate how the technical and contextual affordances of machine learning algorithms may enable these organisations to accomplish that task. We find that, due to the unavailability of high-quality, large training datasets regarding money laundering methods, there is limited scope for using supervised machine learning. Conversely, it is possible to use reinforced machine learning and, to an extent, unsupervised learning, although only to model unusual financial behaviour, not actual money laundering.

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  • Canhoto, Ana Isabel, 2021. "Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective," Journal of Business Research, Elsevier, vol. 131(C), pages 441-452.
    • Handle: RePEc:eee:jbrese:v:131:y:2021:i:c:p:441-452
    DOI: 10.1016/j.jbusres.2020.10.012
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    1. Cristina Alaimo & Jannis Kallinikos, 2017. "Computing the everyday: social media as data platforms," LSE Research Online Documents on Economics 81432, London School of Economics and Political Science, LSE Library.
    2. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    3. Constantiou, Ioanna D & Kallinikos, Jannis, 2015. "New games, new rules: big data and the changing context of strategy," LSE Research Online Documents on Economics 63017, London School of Economics and Political Science, LSE Library.
    4. Hermann Pythagore Pierre Donfouet & P. Wilner Jeanty & Eric Malin, 2018. "Analysing spatial spillovers in corruption: A dynamic spatial panel data approach," Papers in Regional Science, Wiley Blackwell, vol. 97(S1), pages 63-78, March.
    5. Ana Fernández, 2019. "Artificial intelligence in financial services," Economic Bulletin, Banco de España, issue JUN.
    6. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    7. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    8. Paschen, Ulrich & Pitt, Christine & Kietzmann, Jan, 2020. "Artificial intelligence: Building blocks and an innovation typology," Business Horizons, Elsevier, vol. 63(2), pages 147-155.
    9. Ritu Agarwal & Vasant Dhar, 2014. "Editorial —Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research," Information Systems Research, INFORMS, vol. 25(3), pages 443-448, September.
    10. ., 2019. "The future of interest-free financing," Chapters, in: Religion and Finance, chapter 9, pages 193-215, Edward Elgar Publishing.
    11. Edmund Byrne, 2011. "Business Ethics Should Study Illicit Businesses: To Advance Respect for Human Rights," Journal of Business Ethics, Springer, vol. 103(4), pages 497-509, November.
    12. de Moor, A., 2002. "Language/Action meets organisational semiotics : Situating conversations with norms," Other publications TiSEM bbac72a2-840e-4135-8bb4-6, Tilburg University, School of Economics and Management.
    13. Raymond F. Zammuto & Terri L. Griffith & Ann Majchrzak & Deborah J. Dougherty & Samer Faraj, 2007. "Information Technology and the Changing Fabric of Organization," Organization Science, INFORMS, vol. 18(5), pages 749-762, October.
    14. Merendino, Alessandro & Dibb, Sally & Meadows, Maureen & Quinn, Lee & Wilson, David & Simkin, Lyndon & Canhoto, Ana, 2018. "Big data, big decisions: The impact of big data on board level decision-making," Journal of Business Research, Elsevier, vol. 93(C), pages 67-78.
    15. Howard Kunreuther, 2002. "Risk Analysis and Risk Management in an Uncertain World," Risk Analysis, John Wiley & Sons, vol. 22(4), pages 655-664, August.
    16. Aldo de Moor, 2002. "Language/Action Meets Organisational Semiotics: Situating Conversations with Norms," Information Systems Frontiers, Springer, vol. 4(3), pages 257-272, September.
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