Banking sector lack detection: Expectation gap between auditors and bankers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to identify the determinants of the expectation gap in fraud detection between internal auditors and bankers in Indonesia. The shift in the internal audit task in the banking sector can cause the hole in audit expectations to widen. This research uses qualitative methods with an interpretive paradigm which is rarely done by previous research. The results of interviews with internal audit work units and bank managers from 4 state-owned and private banks indicate a gap in audit expectations regarding the responsibilities between internal auditors and bankers, especially in carrying out the function of examining and detecting fraud. This study recommends the financial services authorities and bank leaders be able to improve education regarding anti-fraud policies to stakeholders, especially in terms of a clear division of tasks in fraud detection in the banking sector.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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 it