Fraud risk factors and auditing standards : an integrated identification of a fraud risk management model
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".