The use of generalised audit software by internal audit functions in a developing country: A maturity level assessment
Bibliographic record
Abstract
This article explores the existing practices of internal audit functions in the locally controlled South African banking industry regarding the use of Generalised Audit Software (GAS), against a benchmark developed from recognised data analytic maturity models, in order to assess the current maturity levels of the locally controlled South African banks in the use of this software for tests of controls. The literature review indicates that the use of GAS by internal audit functions is still at a relatively low level of maturity, despite the accelerating adoption of information technology and generation of big data within organisations. The empirical results of this article also confirm that the maturity of the use of GAS by the internal auditors employed by locally controlled South African banks is still lower than expected, given that the world, especially from a business perspective is now fully immersed in a technological-driven business environment. This study has since been extended to other industries in the following countries namely, Canada, Columbia, Portugal and Australia.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".