Can Corporate Governance Structures Reduce Fraudulent Financial Reporting in the Banking Sector? Insights from the Fraud Hexagon Framework
Bibliographic record
Abstract
This study investigates the determinants of Fraudulent Financial Reporting (FFR) in the banking sector from 2021 to 2024 by integrating the Fraud Hexagon framework within a risk and financial management perspective. Using panel data comprising 140 bank-year observations (35 banks over four years), the research applies an empirical analysis to examine six key elements—pressure, opportunity, rationalization, capability, arrogance, and collusion—that shape fraud risk behavior in financial institutions. The results indicate that leverage does not significantly influence fraud incentives, suggesting that financial pressure alone is insufficient to drive fraudulent reporting without weak governance structures. In contrast, factors related to ineffective monitoring, auditor switching, and director change show varying effects on FFR. The findings also reveal that CEO image does not reflect arrogance, which has no significant effect on FFR, and political connections of entities do not automatically reduce fraud risk unless supported by strong and independent governance mechanisms. The study underscores the crucial moderating role of the audit committee in enhancing financial reporting integrity. From a policy perspective, the research provides strategic insights for regulators and supervisory bodies such as the Financial Services Authority (OJK) to strengthen governance frameworks, enforce stricter disclosure requirements, and integrate fraud risk management practices into corporate oversight. Overall, this study contributes to the financial governance literature by demonstrating how effective risk management and governance alignment can reduce fraudulent reporting and improve the sustainability of the banking sector.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".