Financial Fraud and Credit Risk: Illicit Practices and Their Impact on Banking Stability
Bibliographic record
Abstract
The intricate relationship between financial fraud and credit risk, and their combined impact on banking stability, is a vital and under-researched aspect of financial system integrity. To fill this knowledge gap, this study embarked on a thorough bibliometric analysis of the field, utilizing 2790 documents from various sources, including 1853 articles, 504 books, and 177 reviews, spanning the years 1990 to 2023. Utilizing advanced tools, like Biblioshiny and VOSviewer, this study illuminated key geographical, thematic, and intellectual trends, shedding light on an annual growth rate of 13.43% in the related literature and an average citation per document of 28.29. This detailed analysis offered valuable insights into the current research landscape, emphasizing areas such as author collaboration, with 20.32% international co-authorships, and the prevalence of single-authored documents, at 1100. Despite the existing body of research, the interconnected dynamics between financial fraud and credit risk and their implications for banking stability remain underexplored. Therefore, this study sought to unravel this complex relationship and examine its effects at both the micro (individual banks) and macro (banking sector and wider economy) levels. The findings carry significant practical implications, informing policy development, shaping risk management strategies, and contributing to regulatory measures. Despite its limitations, including the potential transformation of identified trends due to evolving financial systems and financial crimes, this study represents a significant contribution to scholarly discourse in the field. It lays the groundwork for future research and facilitates a more secure and resilient banking sector, reflecting the data-driven insights obtained from the research.
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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".