Forensic Accounting: Exploration of Trends and Theme via Bibliometric Analysis
Bibliographic record
Abstract
In today’s technologically advanced era, the demand for forensic accounting has significantly increased, highlighting the growing severity of accounting fraud issues. Forensic accounting has emerged as a crucial field in the detection and prevention of financial fraud, tax evasion, and other financial crimes. This study aims to contribute to the ongoing discourse by uncovering publication trends, identifying prevalent keywords, shedding light on the geographical concentration, and suggesting the future direction of research on forensic accounting. The study is based on a bibliometric analysis of 297 articles from the Scopus database using the TITLE-ABS-KEY approach. Microsoft Excel is used in analysing the frequency of published materials using the corresponding tables and charts. In addition, the VOSviewer software is used to create bibliometric networks and Harzing’s Publish or Perish software is used to assess the citation metrics of the articles. The analysis shows that the number of publications on forensic accounting is increasing, especially between 2020 and 2024. The articles were cited 2914 times, which corresponds to an average of 9.81 citations per article. The results show that the top five common keywords discussed in this area are forensic accounting, fraud, auditing, accounting, and fraud detection, which can be grouped into 5 clusters. The United States, Jordan, Malaysia, Canada, India, and Indonesia are among the countries that contribute to publications in this area. This study offers some insights regarding the future development and advancement of forensic accounting studies in the academic literature of Business, Management and Accounting; Economics, Econometrics and Finance and Social Sciences, as well as provides helpful information for academics and practitioners looking to analyse and delve deeper within this field of research.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.154 | 0.202 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".