Suspicious activity reports: Enhancing the detection of terrorist financing and suspicious transactions in migrant remittances

Abstract

Migrant remittances have become an important factor in poverty alleviation and microeconomic development in low-income nations. Global migrant remittances are expected to exceed US $630 billion by 2023, according to the World Bank. In addition to offering an alternate source of income that supplements the recipient's household earnings, they are less likely to be affected by global economic downturns, ensuring stability and a consistent stream of revenue. However, the ease of global migrant remittance financial transfers has attracted the risk of being abused by terrorist organizations to quickly move and conceal operating cash, hence facilitating terrorist financing. This study aims to develop an unsupervised machine-learning model capable of detecting suspicious financial transactions associated with terrorist financing in migrant remittances. The data used in this study came from a World Bank survey of migrant remitters in Belgium. To understand the natural structures and grouping in the dataset, agglomerative hierarchical clustering and k-prototype clustering techniques were employed. This established the number of clusters present in the dataset making it possible to compare individual migrant remittances in the dataset with their peers. A Structural Equation Model (SEM) and an Local Outlier Factor - Isolation Forest (LOF-IF) algorithm were applied to analyze and detect suspicious transactions in the dataset. A traditional Rule-Based Method (RBM) was also created as a benchmark algorithm that evaluates model performance. 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 applied the proposed ensemble outlier detection model to detect suspicious transactions in the same data set. The proposed ensemble model utilized an Isolation Forest (IF) for pruning and a Local Outlier Factor (LOF) to detect local outliers. The model performed exceptionally well, being able to detect over 90% of suspicious transactions in the testing data set during model cross-validation.