MétaCan
Menu
Back to cohort
Record W1529464615 · doi:10.1108/jfc-04-2013-0028

Misrepresentation of financial statements

2014· article· en· W1529464615 on OpenAlexaff
Cenap Ilter

Bibliographic record

VenueJournal of Financial Crime · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsMacEwan University
Fundersnot available
KeywordsMisrepresentationAuditAccountingFinancial statementBusinessConstructive fraudBalance sheetFinanceActuarial scienceLawPolitical science

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to show the public, in general, and auditors, in particular, that in the absence of control there is always a risk of fraud. Fraud can be done in various forms. Larceny may be the most obvious case of fraud, but fraud may be done in many other ways too. Balance sheet fraud or financial statements fraud is a broader issue; it is far-fetched than a few hundred dollars of a larceny case. In financial statement fraud, the deep down effect may be millions or billions of dollars. Design/methodology/approach – The paper has been designed based on a fraud theory. The author has observed the implications of a possible fraud in a real audit case. The fraud theory has been tested through financial analysis and audit tests. The theory has then been revised and the existence of a financial statement fraud has been proven. Findings – The paper explores that banks and group companies controlled by unreliable owners can lead to misuse of public's funds in accordance with the directives of the owner. Public's money can be transferred to other group companies in an illegal manner – in excessive amounts – and never returned to the bank by means of applying different accounting fraud techniques. Research limitations/implications – Auditors, who may audit group companies that include a bank or banks with deposit receiving and lending rights, should pay attention to the transactions between the group's bank and the other group companies. The lending may be excessive in amount and/or never paid back and the financial statements would be misrepresented covering various fraud schemes. Originality/value – The case that the paper deals with reflects the author's own audit experiences. The names of the companies have been changed but not the essence of the events. From this perspective, it sheds light onto the path of an auditor who happens to be in a similar situation.

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)0.001
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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.

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

Citations14
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Financial CrimeSame topicAuditing, Earnings Management, GovernanceFrench-language works237,207