Operational Risk Management in Banks: A Bibliometric Analysis and Opportunities for Future Research
Bibliographic record
Abstract
The last few years have witnessed tremendous challenges in the management of operational risks faced by banks and the emergence of newer risks. The working models for bank staff are now different; additionally, there has been a massive increase in the digitization level. All these aspects make operational risk management in banks an attractive field of study. There is a need to perform systematic bibliometric analysis in this research area, providing the various trends and highlighting areas for further research analysis. This research paper has examined the various aspects of operational risk management in Banks by performing a thorough bibliometric analysis of 676 articles extracted from two data databases, i.e., Scopus and Web of Science, from 2010 until March 2023. These were analyzed using the tools Biblioshiny and VOSviewer. Various bibliometric techniques like analysis of trends, citations, contributing authors, keywords, and bibliographic coupling have been performed. This research paper has significant theoretical and practical implications which can assist future researchers. Operational risks are ever-dynamic, and five themes, i.e., climate risk, information security risks, geopolitical risks, third-party risks and compliance risks, have been identified in this research paper as key focus areas for conducting research in the future. The findings of this study and suggestions for future research will be useful to academicians, policymakers, and operational risk management professionals for identifying potential areas of collaboration in the future to strengthen the operational risk management framework.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.102 | 0.197 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".