New technology for old crimes? the role of cryptocurrencies in circumventing the global anti-money laundering regime and facilitating transnational crime
Bibliographic record
Abstract
The phenomenon of transnational crimes such as money laundering, drug trafficking, and terrorist financing remains a persistent problem for the international community and for individual states. Though existing efforts to combat transnational crimes are by no means perfect, the recent iteration of financial technology – cryptocurrencies – presents a potential alternative means for circumventing the regulatory measures that inhibit transnational crimes. Most features of traditional banking facilities are absent in cryptocurrencies: transactions therein are considered to be relatively anonymous, cryptocurrencies are also decentralized, and their use lacks any formal oversight. Thus, cryptocurrencies could be considered a further complication to the already challenging problem faced by regulatory and enforcement agencies in striving to combat transnational crimes. To date, the degree to which cryptocurrencies remain susceptible to exploitation for criminal purposes is still the subject of much debate. Therefore, this thesis will contribute to this ongoing conversation by examining the extent to which the use of cryptocurrencies facilitates transnational crimes and in turn circumvent the existing global anti-money laundering (AML) regime. Using a New Legal Realism theoretical lens, this thesis interrogates how the complex international AML framework could be interpreted, in the first instance, to apply to cryptocurrency-facilitated money laundering. This thesis also provides an overview of cryptocurrencies using Bitcoin as a case study. Given the emerging nature of cryptocurrencies, Bitcoin, as the first fully developed and widely used cryptocurrency network, is used to highlight the operating systems of cryptocurrencies. Furthermore, this work draws from the criminological discipline to explain the attractiveness of cryptocurrencies for money laundering to facilitate transnational crime. Relying on a number of criminological theories, this thesis demonstrates the importance of regulating cryptocurrencies while the problem of its illicit use is still at a nascent stage. In this case cryptocurrencies, in the absence of cohesive regulation, could become attractive to criminals seeking alternative avenues to launder the proceeds of their crimes. Thus, this thesis contributes original insights to the discussion of new techniques for facilitating transnational crimes by demonstrating through interpretation, how cryptocurrencies could be brought within the application of the existing AML regime as it is.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".