Continuous auditing: Developing automated audit systems for fraud and error detections
Bibliographic record
Abstract
Indonesian Institute of Certified Public Accountants, American Institute of Certified Public Accountants and the Canadian Institute of Chartered Accountants(SAS 99 sec 110, par 2) establishes auditors responsibility to plan and perform the audit to obtain reasonable assurance about whether the financial statements are free of material mis- statement, whether caused by error or fraud to plan and perform audits to provide a reasonable assurance that the audited financial statements are free of material fraud. This study proposed the development of Automated Audit System model to assist auditors in bridging them to the challenges in detecting fraud. This approach firstly provides a framework to have better understanding about the business process and data structures of information systems which is required in establishing an effective audit program. These ingredients are mapped in the audit process, including audit objectives, internal control and audit rules by using the Use-Case Diagram, Data Flow Diagram and Entity Relationship Diagram. Second, this study employs Ben- fords Law and Automatic Transaction Verification for the detection of anomalies and irregularities to design the framework. It also presents a systematic case study of ac- tual continuous auditing in department stores that using ERP systems. It is expected to detect frauds and errors. It proves that Continuous Audit and Benford Law can establish strong framework in Automated Audit Systems for Fraud Detections and finally provide a big contribution to internal control and company policies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".