 AI culture ‘profiling’ and anti-money laundering: Efficacy vs ethicsJohn W. Goodell, Cal B. Muckley, Parvati Neelakantan, Darragh Ryan and Pei-Shan Yu International Review of Financial Analysis, 2025, vol. 101, issue C Abstract: 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. Keywords: Financial institutions; Anti-money laundering; Machine learning; National culture (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:finana:v:101:y:2025:i:c:s1057521925000675 DOI: 10.1016/j.irfa.2025.103980 Access Statistics for this article International Review of Financial Analysis is currently edited by B.M. Lucey More articles in International Review of Financial Analysis from Elsevier |
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