Money Laundering and the Globalisation of Informal Value Transfer Systems (IVTS)
Bibliographic record
Abstract
Informal value transfer system (IVTS) broker networks have expanded globally, facilitating money laundering and terrorist financing. These systems are frequently synonymised with serious and organised crime and unregulated activity, including drug cartels, human trafficking, and tax evasion. However, most IVTS provide legitimate services that can aid humanitarian causes in remote and developing communities, lower poverty, support social welfare, and reduce the risk of war. This research aimed to contextualise the development of criminal IVTS and the concomitant anti-money laundering challenges, using theoretical frameworks of globalisation and bureaucratisation to inform future IVTS antimoney laundering, intelligence-led, and human rights-compliant policing policies. A qualitative approach was employed to achieve this aim, utilising empirical data from ideographically styled, semi-structured qualitative interviews of informed experts, combined with an extensive review of relevant literature. The findings provide an in-depth and unique insight, partly because the research was carried out by an experienced UK law enforcement financial investigator with first-hand exposure to complex money laundering and access to the knowledge and experience of recognised subject matter experts. The research was also supported by exclusive contributions from Dame Lynne Owens, Director General of the National Crime Agency (2016-2021), made in communique with the author. In summary, this research found, firstly, that the globalisation of IVTS networks has added complexity, transactional distance, and jurisdictional hurdles to money laundering investigations, putting them beyond the scope of most routine policing responses and knowledge; and, secondly, that denying or disrupting IVTS remittance providers in one jurisdiction can undermine innocent people's human rights in another.
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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".