A Robust Newcomb-Benford Account Screening Profiler: An Audit Decision Support System
Bibliographic record
Abstract
The best practices execution of the audit is conditioned by the facility with which Decision Support Systems [DSS] can be created using simple Excel™ programming tools and functionalities. Such DSS can aid in the exclusive binary triage of the many of the client’s accounts each of which typically has tens of thousands of items into: {Accounts that may warrant Extended Procedures Testing [EPT]} or {Accounts that may not warrant EPT}. We use the Newcomb-Benford first-digit-profile as a triage platform to screen client accounts into the above mentioned exclusive sets. We call this DSS: The Newcomb-Benford Robust Screening:DSS [NBRS:DSS]. We report on the details of its development & vetting, and illustrate its functionalities using one of the historical Benford Datasets. The NBRS:DSS employs four account screening platforms each of which has been reported in the literature. The NBRS:DSS is available from the authors free as a download without restrictions to its use.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".