Machine Learning to Enhance the Detection of Terrorist Financing and Suspicious Transactions in Migrant Remittances
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".