University Research
AI culture ‘profiling’ and anti-money laundering: Efficacy vs ethics
Academic department
Department of Finance
Description
Using extensive transaction and money laundering detection data, at a globally important financial institution, we investigate the efficacy of including facets of national culture in formulating anti-money laundering predictions. For corporate and individual accounts, Hofstede individualism scores of the country in which a customer is resident, or from which a wire is sent/received, are of first-order importance in the detection of money laundering. When combined with account and transaction data; as well as even a proprietary institutional algorithm, individualism scores continue to determine the models’ predictive performances. The efficacy of cultural profiling in money laundering detection underscores the need for stringent and enforced data protection to prohibit its use. This will safeguard the civil right of individuals to privacy and promote financial inclusion.
Publisher name
Elsevier
Document Type
Article
Digital Object Identifier (DOI) Link
https://doi.org/10.1016/j.irfa.2025.103980
Publication Date
Winter 2-15-2025
Publication Title
International Review of Financial Analysis
Volume
101
First Page
1
Last Page
15
Recommended Citation
Goodell, John W.; Muckley, Cal B.; Neelakantan, Parvati; and Ryan, Darragh, "AI culture ‘profiling’ and anti-money laundering: Efficacy vs ethics" (2025). University Research. 2.
10.1016/j.irfa.2025.103980
https://ideaexchange.uakron.edu/university_research/2
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.