AI culture ‘profiling’ and anti-money laundering: Efficacy vs ethics

IF 7.5 1区 经济学 Q1 BUSINESS, FINANCE International Review of Financial Analysis Pub Date : 2025-02-15 DOI:10.1016/j.irfa.2025.103980
John W. Goodell , Cal B. Muckley , Parvati Neelakantan , Darragh Ryan , Pei-Shan Yu
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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.
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来源期刊
CiteScore
10.30
自引率
9.80%
发文量
366
期刊介绍: The International Review of Financial Analysis (IRFA) is an impartial refereed journal designed to serve as a platform for high-quality financial research. It welcomes a diverse range of financial research topics and maintains an unbiased selection process. While not limited to U.S.-centric subjects, IRFA, as its title suggests, is open to valuable research contributions from around the world.
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