Trade-based money laundering: organized crime, learning and international trade
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the link between trade-based money laundering and organized crime. Trade-based money laundering (TBML) has emerged as the newest and possibly most complex method used by organized crime and white-collar crime groups for illegally laundering money in the international financial system. Using legitimate global trade streams, criminal organizations are able to transfer billions of dollars annually between jurisdictions without having to adhere to state-level currency regulations. Design/methodology/approach Using a rational approach to understanding the behavior of criminal organizations, it is argued that TBML will continue to grow as a preferred methodology for laundering money internationally. As criminal organizations continue to be displaced from the more traditional methods of money laundering, they will look for and find TBML as a viable alternative for moving money between different jurisdictions. Findings As the methodology becomes more developed, the skill set will transfer to an increasing number of organized crime groups and be incorporated as a mainstream method for laundering and moving money. Practical implications To stay current with contemporary money laundering schemes, law enforcement agencies will have to train their investigators to spot, investigate and collect requisite evidence for successful prosecution and disruption of TBML offences. Moreover, in the absence of a global regime for sharing trade and customs information, legislators and law enforcement agencies will have to consider how to best expedite the sharing of trade and customs information. Originality/value This is the only study to address TBML as an emerging money laundering technique and the transfer of the skill between organized crime groups. It further details the skills that police investigators needs to develop to successfully combat TBML.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".