Financial Statement Fraud Detection With Beneish M-Score and Dechow F-Score Model: An Empirical Analysis of Fraud Pentagon Theory in Indonesia
Bibliographic record
Abstract
This research contributes to the Financial Statement Fraud (FSF) literature by examining the ability of the Beneish model and the F-Score model to detect FSF trends in the Indonesian context. This study also aims to provide empirical evidence on other issues that encourage fraud. The results of this study are empirical evidence that the financial target variables and CEO narcissism have a significant effect on financial statement fraud while financial stability, external pressure, supervision ineffectiveness, related party transactions, auditor turnover, and CEO dominance have no significant effect on financial statement fraud. Furthermore, when viewed in the table of the F-Score and M-Score models, there are several companies suspected or indicated of fraudulent financial reporting, including 284 companies out of 385 observation samples. The percentage of companies indicated to have financial statements fraud requires further examination to really prove that the company is cheating. The results of the fraudulent financial report analysis using the F-Score dan M-score for manufacturing companies in 2014 - 2018 successfully analyzed a total of 284 companies that indicated fraudulent financial reporting.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".