Predicting Money Laundering Using Machine Learning and Artificial Neural Networks Algorithms in Banks

This paper aims to build a machine learning and a neural network model to detect the probability of money laundering in banks. The paper's data came from a simulation of actual transactions flagged for money laundering in Middle Eastern banks. The main findings highlight that criminal networks...

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Bibliographic Details
Published inJournal of applied security research Vol. 19; no. 1; pp. 20 - 44
Main Author Lokanan, Mark E.
Format Journal Article
LanguageEnglish
Published Routledge 02.01.2024
Subjects
anti-money laundering
artificial intelligence
banks
Machine learning
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ISSN1936-1610
1936-1629
DOI10.1080/19361610.2022.2114744

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Summary:This paper aims to build a machine learning and a neural network model to detect the probability of money laundering in banks. The paper's data came from a simulation of actual transactions flagged for money laundering in Middle Eastern banks. The main findings highlight that criminal networks mainly use the integration stage to integrate money into the financial system. Fraudsters prefer to launder funds in the early hours, morning followed by the business day's afternoon time intervals. Additionally, the Naïve Bayes and Random Forest classifiers were identified as the two best-performing models to predict bank money laundering transactions.
ISSN:1936-1610
1936-1629
DOI:10.1080/19361610.2022.2114744