A Comparative Analysis of the Standard of Fraud Required Under the Fraud Rule in Letter of Credit Law
Bibliographic record
Abstract
National courts have required different standards of fraud to justify non-payment, or restraint of payment, under a letter of credit.The United Nations Commission on Trade Law (UNCITRAL) has adopted its own position.The issue is far from settled in any legal system.Based on an analysis of the law in the United States, United Kingdom, Canada and Australia, and under the Convention, this article proposes a standard that is a distinct improvement on the various standards applied around the world and suggests a means for its implementation.The fraud rule allows the issuer of a letter of credit or a court to disrupt the payment of a letter of credit when fraud is involved.The raison d'etre of letters of credit is to provide an absolute assurance of payment to a seller, provided the seller presents documents that comply with the terms of the credit.The fraud rule thus goes to the very heart of the letter of credit obligation.The fraud rule is necessary to limit the activities of fraudsters, but its scope must be carefully circumscribed so as not to deny commercial utility to an instrument that exists to serve as an assurance of payment. 1 This article explores the kind of fraud required to invoke the fraud rule or, in other words, what does fraud mean under the fraud rule in the law governing letters of credit?This is a challenging ques-
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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 | 0.019 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".