The anti-money laundering risk assessment: A probabilistic approach

Henry Ogbeide, Mary Elizabeth Thomson, Mustafa Sinan Gonul, Andrew Castairs Pollock, Sanjay Bhowmick and Abdullahi Usman Bello

Journal of Business Research, 2023, vol. 162, issue C

Abstract: Since the 1980s, researchers and practitioners examining the vulnerabilities of financial institutions to money laundering risk have offered some insights on how experts conduct anti-money laundering risk assessments. A common theme in the risk assessment literature is the emphasis on box-ticking rather than exercising judgment case-by-case, which has influenced our consideration of whether experts in this domain are immune to cognitive biases that novices can be vulnerable to during risk assessment. We found that both experts and novices were overconfident about their distribution judgments and this effect was slightly more pronounced in the expert group. One manifestation of the overconfidence effect in both groups was the preference for false-positive over false-negative errors. Notably, novice participants slightly outperformed expert participants in the proportion of correct outcomes. A feedback mechanism that is effective at alleviating biases, improving processes, and resultant judgment accuracy may be valuable to experts in this domain.

Keywords: Financial institutions; Anti- money laundering; Expertise; Probability judgment; Bias; Overconfidence (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:162:y:2023:i:c:s0148296323001789

DOI: 10.1016/j.jbusres.2023.113820

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