Using Benford’s Law to Detect Suspected Creative Accounting: A Simple Tool for Small Accounting Firms in Emerging Economies
Bibliographic record
Abstract
Large data series collected over an extended period of time have a tendency toward conformity with Benford’s law in the first few digits. According to this law, the first digit in a group of more than 1,000 numbers is equivalent to 1 in 30.10% of cases, 2 in 17.60% of cases, with the probability of appearance logarithmically decreasing as the first digit increases This study details a straightforward yet effective audit procedure employed by a small accounting firm in an Argentinean provincial economy to identify mistakes and raise concerns about possible creative accounting by clients. Through this assurance procedure, the audit firm examines client-provided data to determine whether Benford’s law is being followed. Excel spreadsheets’ automated features are employed for this. To illustrate the tool’s usefulness, the annual sales of three of the firm’s clients were examined. The findings indicate that every client has a distinct profile. Client 1 fully complies with Benford’s law, leading to an unqualified opinion in the auditor’s report; Client 2 continues to comply with Benford’s law but raises some concerns that should be discussed with management before issuing the audit opinion; and Client 3 exhibits low conformance and raises several red flags that require discussion with management. This work introduces granular data, which allows for the development of tenable hypotheses for the observed nonconformity, such as product mix and inflation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".