A Comparison Study of Risk-based Auditing in Four Developed Countries
Bibliographic record
Abstract
Purpose: This study aimed to identify and compare the best practices of a risk-based audit (RBA) approach used in four developed countries. Design/ Methodology/ Approach: A thorough library search and examination of the literature on the RBA approach was conducted. Denmark, Australia, Canada, and the United Kingdom were selected based on their excellent positions in the Corruption Perception Index 2022 ranking. Findings: The four countries adopted the Institute of Internal Audit standards in conducting their RBA. All four countries, except Denmark, used the ISO 31000:2018 as a guidance framework. Furthermore, the four countries used comparable risk determination, assessment, and control techniques. Additionally, the RBA risk governance structure of the four countries is based on the Three Lines of Defence concept. Research Limitations/ Implications: Most of the data were from secondary sources. Only four countries were compared and were selected using only one index. Additionally, the study focused on internal auditing practices as a public governance tool. Practical Implications: The results presented the opportunity for government internal auditors to reconsider and enhance auditing procedures to increase public sector delivery system efficacy. Originality/ Value: This article covers the RBA used by internal auditors in four developed countries. The results could catalyse investigations into the efficacy and best practices of additional elements in public sector governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".