Unraveling Financial Fraud: The Role of the Board of Directors and External Advisors in Conducting Independent Internal Investigations*
Bibliographic record
Abstract
ABSTRACT Although firms are encouraged by the SEC and Department of Justice to conduct internal investigations following financial misconduct, prior research finds few benefits for investigating firms. This study examines a novel aspect of internal investigations—namely, whether the investigation is conducted by independent versus nonindependent teams—and explores the impact of these teams on investigation outcomes. Consistent with our predictions, we find that firms whose internal investigations are led by independent teams are more likely to retain external advisors, have a higher likelihood of CEO turnover, and face a lower likelihood of an SEC enforcement action than do firms whose investigations are led by nonindependent teams. Our findings demonstrate that the SEC grants enforcement leniency to firms that conduct an internal investigation, but this finding only holds when the investigation leader is considered independent. These results also suggest that appointing independent groups to lead internal investigations protects the firm, at the expense of the CEO, following accounting fraud. Our paper has important implications for researchers studying accounting irregularities as we are the first to show that independent board members and external advisors play a direct role in the resolution of financial misconduct through their job on the internal investigation team.
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 | 0.034 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".