Examining Documentation Tools for Audit and Forensic Accounting Investigations
Bibliographic record
Abstract
This study examines some of the documentation tools and techniques that forensic accountants, internal auditors, external auditors, and others can use to document accounting and financial reporting systems under investigation. While prior research has addressed these items piecemeal, this study is the first to incorporate them, along with current related research and theoretical foundation, and relate them in aggregate to the work of forensic accountants, internal auditors, external auditors, and others. Inputs, processes, and outputs of modern accounting and financial reporting systems are often difficult to fully grasp, with weaknesses obscured by the complexities of the system. These weaknesses make a system vulnerable to fraudsters, embezzlers, hackers, and others who will take advantage of system weaknesses to perpetrate financial fraud, embezzlement, or other financial crimes. Documentation tools and techniques examined in this study will be useful to forensic accountants, internal auditors, external auditors, and others for identifying the components, processes, and potential weaknesses of accounting and financial reporting systems.
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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 | 0.044 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".