Predicting Suspicious Money Laundering Transactions using Machine Learning Algorithms
Bibliographic record
Abstract
Abstract This study employs machine learning techniques to identify key drivers of suspicious activity reporting. The data for this study comes from all suspicious activities reported to the California government in 2018. In total, there were 45,000 records of data that represent various features. The paper uses linear regression along with Lasso, Ridge, and Elastic Net to perform feature regularization and address overfitting with the data. Other probabilistic and non-linear algorithms, namely, support vector machines, random forests, XGBoost, and CatBoost, were used to deal with the complexity of the data. The results from the mean squared and root mean squared errors indicate that the ensemble tree-based algorithm performed better than the statistical and probabilistic models. The findings revealed that filings from regulators, the type of products, and customers' relationships with the institutions were the top contributors to SAR filings. Through the evaluation of a vast amount of data, this study provides valuable insights for identifying suspicious activities in financial transactions and has the potential to significantly improve suspicious transaction monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".