DOUBLE INVOICING IN INTERNATIONAL TRADE: THE FRAUD AND NULLITY EXCEPTIONS IN LETTERS OF CREDIT – ARE THE AMERICA ACCORD AND THE UCP 500 CROOKS’ CHARTERS!?
Bibliographic record
Abstract
This article: First, (a) re-examines the fraud exception rule in letters of credit transactions with specific reference to the United City Merchants v Royal Bank of Canada (the American Accord) and against the background of a recent commonwealth decision accepting nullity as a new exception; (b) evaluates its impact on over/under invoicing under the WTO Agreement on Pre-shipment Inspection of Goods in International Trade (PSI); and (c) assesses its implication on the IMF Agreement on Exchange Control implemented in the UK by the IMF Agreement Regulations 1946 made under the IMF Agreement Acts 1945 as amended. Secondly, it argues that the current UCP 500 is outmoded and inadequate to meet current needs and is therefore in need of urgent revision. Thirdly, it recommends, inter alia, that in accordance with the said commonwealth decision, fraud by third parties should be recognised by English law as an independent and separate nullity exception. Fourth, and finally, it concludes that the status-quo acts as an unwitting Crooks’ Charter for money launderers, documentary fraudsters and other white collar crimes.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".