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Record W2289443796 · doi:10.14288/1.0077716

Standby letters of credit and fraud

2010· article· en· W2289443796 on OpenAlexaffabout
Pierre Sigrist

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBusinessComputer securityComputer scienceActuarial science

Abstract

fetched live from OpenAlex

During the last ten years, there has been a remarkable increase in the number of cases involving incidents of fraud in standby letters of credit. The purpose of this thesis is to examine the effect of fraud on standby letters of credit transactions. This study principally deals with standby letters of credit issued under the 1983 Revision of the Uniform Customs and Practice for Documentary Credits, drafted by the International Chamber of Commerce, which is a set of internationally recognized rules for documentary credit operations. Due to the international character of letters of credit law, I have adopted a comparative approach that deals with materials from the U.S., Canada, U.K., Germany and Switzerland. This thesis will first show that there are two types of standby letters of credit, which have to be distinguished because they involve different obligations and risks for the parties. A device payable against the beneficiary's simple statement will be described as a "simple statement" standby credit, whereas a device payable against a set of documents will be called a "documentary" standby credit. The thesis will then demonstrate why the treatment of fraud should not be the same for "simple statement" standby credits and "documentary" standby credits.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.161
Teacher spread0.152 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2010
Admission routes2
Has abstractyes

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