Machine Learning to Enhance the Detection of Terrorist Financing and Suspicious Transactions in Migrant Remittances

Stanley Munamato Mbiva () and Fabio Mathias Correa
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Stanley Munamato Mbiva: Department of Statistics, Rhodes University, Makanda, Eastern Cape 6139, South Africa
Fabio Mathias Correa: Department of Mathematical Statistics and Actuarial Sciences, University of the Free State, 9302 Bloemfontein, South Africa

JRFM, 2024, vol. 17, issue 5, 1-19

Abstract: Migrant remittances have become significant in poverty alleviation and microeconomic development in low-income countries. However, the ease of conducting global migrant remittance transfers has also introduced the risk of misuse by terrorist organizations to quickly move and conceal operational funds, facilitating terrorism financing. This study aims to develop an unsupervised machine learning algorithm capable of detecting suspicious financial transactions associated with terrorist financing in migrant remittances. To achieve this goal, a structural equation model (SEM) and an outlier detection algorithm were developed to analyze and identify suspicious transactions among the financial activities of migrants residing in Belgium. The results show that the SEM model classifies a significantly high number of transactions as suspicious, making it prone to detecting false positives. Finally, the study developed an ensemble outlier detection algorithm that comprises an isolation forest (IF) and a local outlier factor (LOF) to detect suspicious transactions in the same dataset. The model performed exceptionally well, being able to detect over 90% of suspicious transactions.

Keywords: machine learning; isolation forest; outliers; structural equation modeling (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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