Analysing the efficiency of computer-assisted audit tools and techniques within the Ekurhuleni Metropolitan Municipality
Bibliographic record
Abstract
This research investigates Computer-Assisted Audit Techniques (CAATTs) within the Internal Audit Unit at the City of Ekurhuleni Metropolitan Municipality (EMM). The study explores the extent to which CAATTs are utilized, the challenges faced in their implementation, and their impact on the efficiency and effectiveness of internal audits. Data was collected through a mixed-methods approach, including surveys and interviews with internal auditors. The findings reveal that while CAATTs, primarily TeamMate, are increasingly being used to automate audit processes, their adoption is hindered by limited access, insufficient training, and high costs. Despite these challenges, CAATTs have been shown to improve audit accuracy and efficiency, contributing to more timely and reliable audit outcomes. The study highlights the need for enhanced IT infrastructure, greater executive support, and comprehensive training to optimize the use of CAATTs. Based on these findings, the research provides several recommendations, including expanding the use of diverse CAATTs, investing in IT support, and fostering a culture of innovation to maximize the benefits of these technologies in internal auditing. This study contributes to the growing body of knowledge on CAATTs and offers practical insights for municipal internal audit units seeking to enhance their audit capabilities through technology.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".