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Record W3162021

Fraud risk factors and auditing standards : an integrated identification of a fraud risk management model

2008· article· en· W3162021 on OpenAlexvenueno aff
Tumpal Wagner Sitorus

Bibliographic record

VenueThe Journal of Rheumatology · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsAuditBusinessRisk managementRisk assessmentIdentification (biology)Risk analysis (engineering)AccountingAudit riskInternal controlActuarial scienceComputer securityComputer scienceFinance
DOInot available

Abstract

fetched live from OpenAlex

The aims of this thesis are to set out the ten audit outcome based scenarios and the research questions and auditing problems derived from the scenarios, to develop a wide fraud framework and two hypothesised models of fraud symptoms, and to examine the questions and problems using literature review studies and the models using structural equations modelling. This approach is in accordance with the direct call for the use of more advanced statistical methods by Michael & Adler (1971), Steane & Cockerell (2005), and Zahra et al. (2005). The thesis uses a range of references drawn from the fields of economics, finance, auditing, criminology, law, psychology, organisational behaviour and research methodology.\nIt finds that the fraud risk factors listed in the International Standards on Auditing (ISA) 240, have only been drawn from the findings of Cressey (1950, 1973), and that later models proposed by Krambia-Kapardis (1999, 2001, 2002), for instance, have still not fully explained the aetiology of fraud and the complexity of all forms of fraud and corruption (Wells, 1997, 2005, 2007).\nThree additional fraud risk factors, namely collusion, justice avoidance, and organisational orientation, were included in an examination of two hypothesised models that incorporated rationalisation into causal relationships within a fraud commission model and hence of a pre-fraud risk management model.\nA half-sample of 122 Indonesian respondents, who had ever encountered fraud or corrupt practices, was used to test two theory based structural equations models. Because of the poor fit of the two models to the data as shown by the Standardized Root Mean Residual (SRMR) index and because the path between rationalisation and commission of fraud was found to be non-significant, an exploratory research process was used to derive a post-hoc model. The outcome of this process was the introduction of additional paths into the second model.\nThe post-hoc model was tested using another half-sample of 122 respondents and produced a good fit to the data. Significant direct and indirect drivers of commission of fraud were identified and these extended the theory, introduced a wider range of fraud risk factors for consideration by the International Federation of Accountants (IFAC) and the Public Company Accounting Oversight Board (PCAOB), for instance, and called for both the establishment of an integrated mechanism by audit and justice institutions and more integrated curriculum.\nCollusion was perceived to be the strongest direct influence on commission of fraud with a lesser effect arising from opportunity for fraud and a final direct influence arising from the avoidance of justice. In addition, organisational orientation was perceived to provide another indirect influence on the fraud commission.\nThe overall findings in regard to all of the research questions and problems, theoretical models, and the search for a more robust methodology have provided guidance for the expansion of the consideration of fraud risk factors and hence of fraud risk theories, for the more robust prescription to overcome the fraud symptoms, and for the stronger solution to resolve problems and failures, hence the eight recommendations that can be proffered. These should be taken into consideration by the accounting profession and auditing (self-) regulators (e.g., Indonesian Audit Board), fraud and auditing researchers, practitioners, fraud experts, criminologist, academia or authorities (e.g., Indonesian justice institutions).\nKey Words: Fraud risk factors, collusion, justice avoidance, organisational orientation, auditing standards setters, structural equations modelling.\nData Availability: For data and a potentially collaborative study (using worldwide data), contact the author.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.288
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2008
Admission routes1
Has abstractyes

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