 Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspectiveAna Isabel Canhoto Journal of Business Research, 2021, vol. 131, issue C, 441-452 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. Keywords: Big data; Artificial intelligence; Machine learning; Algorithm; Customer profiling; Financial services; Anti-money laundering; United Nations; Sustainable development goals (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:131:y:2021:i:c:p:441-452 DOI: 10.1016/j.jbusres.2020.10.012 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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