Flexible inverse adaptive fuzzy inference model to identify the evolution of operational value at risk for improving operational risk management
- Submitting institution
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De Montfort University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11061
- Type
- D - Journal article
- DOI
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10.1016/j.asoc.2018.01.024
- Title of journal
- Applied Soft Computing
- Article number
- -
- First page
- 614
- Volume
- 65
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
-
-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Operational risk was critical in the 2008 global financial crisis due to limited existing models to explain and estimate it from highly qualitative information about failures in financial organizations. Our novel contribution enables the characterization of a risk event and estimating the loss distribution and the associated operational value at risk. Our model works with highly qualitative data, connecting the risk measurement with risk management, mitigating the lack of available historical data; a problem natural to low occurrence and highly undesirable events. Being first to provide loss distribution capabilities under different risk profiles explaining the evolution of risk in real-time.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -