The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective
Open accessAbstract
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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Cites
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
Mixing Qualitative and Quantitative Methods: Triangulation in Action.
Jick, T. D. (1979) · Administrative Science Quarterly · doi:10.2307/2392366 · Cited by 7,103 · cited 1×
“This approach helped to recognise converging findings, and increased the robustness of the analysis (Jick, 1979).”
ResultSorting Things Out: Classification and Its Consequences
Bowker, G. C., & Star, S. L. (1999) · doi:10.7551/mitpress/6352.001.0001 · Cited by 6,188
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 4,567 · cited 3×
“This problem is particularly relevant in the case of predictive analytics, where analysts are unable to assess the quality of the output prior to implementation and scaling (Mittelstadt, Allo, Taddeo, Wachter, & Floridi, 2016).”
BackgroundArtificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978
Big other: Surveillance Capitalism and the Prospects of an Information Civilization
Zuboff, S. (2015) · Journal of Information Technology · doi:10.1057/jit.2015.5 · Cited by 3,205 · cited 2×
“Given the scale of continued expansion of customer surveillance by commercial organisations for their own strategic purposes (Zuboff, 2015), or on behalf of governments (Ball et al., 2015), the possibility of errors and associated consequences is hugely magnified.”
BackgroundBig data analytics and firm performance: Effects of dynamic capabilities
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.009 · Cited by 2,474
Technologies, texts and affordances.
Hutchby, I. (2001) · Sociology · doi:10.1177/s0038038501000219 · Cited by 2,012
Leonardi, P. M. (2011) · MIS Quarterly · doi:10.2307/23043493 · Cited by 1,889
Cited by
Kulkov, I., Kulkova, J., Rohrbeck, R., Menvielle, L., Kaartemo, V., & Makkonen, H. (2024) · Sustainable Development · doi:10.1002/sd.2773 · Cited by 420
Kute, D. V., Pradhan, B., Shukla, N., & Alamri, A. (2021) · IEEE Access · doi:10.1109/access.2021.3086230 · Cited by 209
International Business, digital technologies and sustainable development: Connecting the dots
Ciulli, F., & Kolk, A. (2023) · Journal of World Business · doi:10.1016/j.jwb.2023.101445 · Cited by 86
Metadata
- Authors
- Canhoto, A. I.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 131
- Pages
- 441-452
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W3093195402
- Cited by
- 171
- References
- 17
- FWCI
- 28.42
- Type
- journal article
- Language
- en
- Open access
- green
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