Operational Risk Management: A Practical Approach to Intelligent Data Analysis
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Operational Risk Management: a practical approach to intelligent data analysis provides practical and tested methodologies for combining structured and unstructured, semantic-based data, and numeric data, in Operational Risk Management (OpR) data analysis.
- The book is presented in four parts: 1) Introduction to OpR Management, 2) Data for OpR Management, 3) OpR Analytics and 4) OpR Applications and its Integration with other Disciplines.
- Explores integration of semantic, unstructured textual data, in Operational Risk Management.
- Provides novel techniques for combining qualitative and quantitative information to assess risks and design mitigation strategies.
- Presents a comprehensive treatment of "near-misses" data and incidents in Operational Risk Management.
- Looks at case studies in the financial and industrial sector.
- Discusses application of ontology engineering to model knowledge used in Operational Risk Management.
Many real life examples are presented, mostly based on the MUSING project co-funded by the EU FP6 Information Society Technology Programme. It provides a unique multidisciplinary perspective on the important and evolving topic of Operational Risk Management. The book will be useful to operational risk practitioners, risk managers in banks, hospitals and industry looking for modern approaches to risk management that combine an analysis of structured and unstructured data. The book will also benefit academics interested in research in this field, looking for techniques developed in response to real world problems.