The impact of artificial intelligence on the quality of external auditing in Jordanian commercial banks: The mediating role of the quality of financial reports
Bibliographic record
Abstract
The current problem of the study is to explore the mediating role of financial reporting quali-ty in the impact of artificial intelligence (AI) on the quality of external auditing in Jordanian commercial banks. A descriptive analytical approach was used. The target population in this research consists of all 13 Jordanian commercial banks listed on the Amman Stock Ex-change. The researcher was able to collect 198 questionnaires that were approved to be filled out by employees of Jordanian commercial banks. The present research in Jordanian com-mercial banks discovered that the impact of AI on external auditing quality is moderated by the quality of financial reporting. The study recommends paying attention to the quality of external auditing, as the auditing process must be carried out efficiently and effectively in accordance with auditing standards. In order for errors and violations discovered during the audit process to be detected, the quality of financial reports must be audited. It also high-lights the need to enhance the use of artificial intelligence in the bank to raise the efficiency of the banking systems and thus raise the bank’s efficiency.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| 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".