From Dirty Money to Luxury Goods: Money Laundering in UK Luxury Goods Sectors
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 PhD thesis examines money laundering risks within UK luxury goods sectors and identifies ways in which these risks can be significantly reduced. In analysing the UK anti money laundering (AML) regime, the thesis adopts a thematic approach based upon the obligations placed on dealers within the MLRs. In particular, the analysis is conducted in relation to obliged entities, registration, the risk-based approach, Customer Due Diligence (CDD), Suspicious Activity Reporting (SAR), and supervision. The thesis employs a mixed methodology which includes doctrinal and qualitative empirical research methods. The qualitative empirical study gathers insights from dealers with semi-structured interviews. \n \nThe study is organised into five parts. The first part provides the theoretical background for the study. This includes consideration of the international and national legislation and policies impacting the UK AML regime. The second part acknowledges money laundering risks in relation to the obligations contained within the MLR and the application of the regime within UK luxury goods sectors. The third part examines compliance challenges faced by dealers in implementing the MLRs. The fourth part considers practices within AML regimes in the US, Cayman Islands, Trinidad and Tobago, Japan, and Canada, which are helpful in evaluating the issues identified within the UK. The final part advances proposals to reduce money laundering risk within UK luxury goods sectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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