Operational Risk Management A Practical Approach to Intelligent Data Analysis
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Statistics in Practice
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Sprache:Englisch
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inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
20.06.2011
Herausgeber
Ron S. Kenett + weitereVerlag
WileySeitenzahl
324 (Printausgabe)
Dateigröße
6030 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9781119956723
Models and methods for operational risks assessment and mitigation
are gaining importance in financial institutions, healthcare
organizations, industry, businesses and organisations in general.
This book introduces modern Operational Risk Management and
describes how various data sources of different types, both numeric
and semantic sources such as text can be integrated and analyzed.
The book also demonstrates how Operational Risk Management is
synergetic to other risk management activities such as Financial
Risk Management and Safety Management.
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.
Key Features:
* 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.
are gaining importance in financial institutions, healthcare
organizations, industry, businesses and organisations in general.
This book introduces modern Operational Risk Management and
describes how various data sources of different types, both numeric
and semantic sources such as text can be integrated and analyzed.
The book also demonstrates how Operational Risk Management is
synergetic to other risk management activities such as Financial
Risk Management and Safety Management.
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.
Key Features:
* 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.
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