Predicting money laundering sanctions using machine learning algorithms and artificial neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article used machine learning (ML) and artificial neural network (ANN) algorithms to predict the likelihood of a country being sanctioned by the Basel Institute on Governance for not adhering to anti-money laundering (AML) standards. Data for this paper came from the Basel AML Index and the World Bank. The results showed that the logistic regression and support vector machine (SVM) classifiers had the highest performance and balanced accuracy scores in sanction prediction. Additionally, these two algorithms also had the highest precision, specificity, and F1 scores, indicating that they were robust in their predictions of money laundering sanctions. In contrast to the ML classifiers, the ANN model had the highest sensitivity and receiver operating characteristic scores for money laundering sanctions. The strongest predictors of sanctions are financial transparency, political and legal risks, unemployment rate, and money laundering and terrorist financing risks. These findings reinforce the potential practical applications of ML and ANN models in predicting sanctions.
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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 | 0.001 | 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 it