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Operational Risk Management: A Practical Approach to Intelligent Data Analysis [Kõva köide]

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Edited by (KPA Ltd, Israel), Edited by (KPA Ltd, Israel)
  • Formaat: Hardback, 336 pages, kõrgus x laius x paksus: 236x156x22 mm, kaal: 603 g
  • Sari: Statistics in Practice
  • Ilmumisaeg: 08-Oct-2010
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 047074748X
  • ISBN-13: 9780470747483
Teised raamatud teemal:
Operational Risk Management: A Practical Approach to Intelligent Data AnalysisSuurem pilt
  • Formaat: Hardback, 336 pages, kõrgus x laius x paksus: 236x156x22 mm, kaal: 603 g
  • Sari: Statistics in Practice
  • Ilmumisaeg: 08-Oct-2010
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 047074748X
  • ISBN-13: 9780470747483
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470747483.html
Märksõnad:
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.

Arvustused

"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." (MyCFO, 25 January 2011)

Foreword xiii
Preface xv
Introduction xvii
Notes on Contributors xxv
List of Acronyms
xxxv
PART I INTRODUCTION TO OPERATIONAL RISK MANAGEMENT
1(38)
1 Risk management: a general view
3(16)
Ron S. Kenett
Richard Pike
Yossi Raanan
1.1 Introduction
3(5)
1.2 Definitions of risk
8(1)
1.3 Impact of risk
9(1)
1.4 Types of risk
9(1)
1.5 Enterprise risk management
10(1)
1.6 State of the art in enterprise risk management
11(4)
1.6.1 The negative impact of risk silos
11(2)
1.6.2 Technology's critical role
13(1)
1.6.3 Bringing business into the fold
14(1)
1.7 Summary
15(2)
References
17(2)
2 Operational risk management: an overview
19(20)
Yossi Raanan
Ron S. Kenett
Richard Pike
2.1 Introduction
19(1)
2.2 Definitions of operational risk management
20(2)
2.3 Operational risk management techniques
22(8)
2.3.1 Risk identification
22(2)
2.3.2 Control assurance
24(1)
2.3.3 Risk event capture
25(1)
2.3.4 Risk and control assessments
25(2)
2.3.5 Key risk indicators
27(1)
2.3.6 Issues and action management
28(1)
2.3.7 Risk mitigation
29(1)
2.4 Operational risk statistical models
30(2)
2.5 Operational risk measurement techniques
32(3)
2.5.1 The loss distribution approach
32(1)
2.5.2 Scenarios
33(1)
2.5.3 Balanced scorecards
34(1)
2.6 Summary
35(2)
References
37(2)
PART II DATA FOR OPERATIONAL RISK MANAGEMENT AND ITS HANDLING
39(86)
3 Ontology-based modelling and reasoning in operational risks
41(20)
Christian Leibold
Hans-Ulrich Krieger
Marcus Spies
3.1 Introduction
41(6)
3.1.1 Modules
43(1)
3.1.2 Conceptual model
43(4)
3.2 Generic and axiomatic ontologies
47(3)
3.2.1 Proton extension
47(1)
3.2.2 Temporal ontologies
48(2)
3.3 Domain-independent ontologies
50(4)
3.3.1 Company ontology
50(4)
3.4 Standard reference ontologies
54(2)
3.4.1 XBRL
54(1)
3.4.2 BACH
55(1)
3.4.3 NACE
55(1)
3.5 Operational risk management
56(2)
3.5.1 IT operational risks
56(2)
3.6 Summary
58(1)
References
58(3)
4 Semantic analysis of textual input
61(18)
Horacio Saggion
Thierry Declerck
Kalina Bontcheva
4.1 Introduction
61(1)
4.2 Information extraction
62(3)
4.2.1 Named entity recognition
64(1)
4.3 The general architecture for text engineering
65(1)
4.4 Text analysis components
66(4)
4.4.1 Document structure identification
66(1)
4.4.2 Tokenisation
67(1)
4.4.3 Sentence identification
67(1)
4.4.4 Part of speech tagging
67(1)
4.4.5 Morphological analysis
68(1)
4.4.6 Stemming
68(1)
4.4.7 Gazetteer lookup
68(1)
4.4.8 Name recognition
68(1)
4.4.9 Orthographic co-reference
69(1)
4.4.10 Parsing
70(1)
4.5 Ontology support
70(3)
4.6 Ontology-based information extraction
73(2)
4.6.1 An example application: market scan
74(1)
4.7 Evaluation
75(1)
4.8 Summary
76(1)
References
77(2)
5 A case study of ETL for operational risks
79(20)
Valerio Grossi
Andrea Romei
5.1 Introduction
79(2)
5.2 ETL (Extract, Transform and Load)
81(3)
5.2.1 Related work
82(1)
5.2.2 Modeling the conceptual ETL work
82(1)
5.2.3 Modeling the execution of ETL
83(1)
5.2.4 Pentaho data integration
83(1)
5.3 Case study specification
84(7)
5.3.1 Application scenario
84(1)
5.3.2 Data sources
85(2)
5.3.3 Data merging for risk assessment
87(2)
5.3.4 The issues of data merging in Musing
89(2)
5.4 The ETL-based solution
91(4)
5.4.1 Implementing the `map merger' activity
92(1)
5.4.2 Implementing the `alarms merger' activity
93(1)
5.4.3 Implementing the `financial merger' activity
94(1)
5.5 Summary
95(1)
References
95(4)
6 Risk-based testing of web services
99(26)
Xiaoying Bai
Ron S. Kenett
6.1 Introduction
99(4)
6.2 Background
103(3)
6.2.1 Risk-based testing
103(1)
6.2.2 Web services progressive group testing
104(1)
6.2.3 Semantic web services
105(1)
6.3 Problem statement
106(1)
6.4 Risk assessment
107(7)
6.4.1 Semantic web services analysis
107(3)
6.4.2 Failure probability estimation
110(2)
6.4.3 Importance estimation
112(2)
6.5 Risk-based adaptive group testing
114(3)
6.5.1 Adaptive measurement
115(2)
6.5.2 Adaptation rules
117(1)
6.6 Evaluation
117(1)
6.7 Summary
118(3)
References
121(4)
PART III OPERATIONAL RISK ANALYTICS
125(44)
7 Scoring models for operational risks
127(10)
Paolo Giudici
7.1 Background
127(1)
7.2 Actuarial methods
128(2)
7.3 Scorecard models
130(3)
7.4 Integrated scorecard models
133(1)
7.5 Summary
134(1)
References
134(3)
8 Bayesian merging and calibration for operational risks
137(12)
Silvia Figini
8.1 Introduction
137(1)
8.2 Methodological proposal
138(3)
8.3 Application
141(7)
8.4 Summary
148(1)
References
148(1)
9 Measures of association applied to operational risks
149(20)
Ron S. Kenett
Silvia Salini
9.1 Introduction
149(4)
9.2 The arules R script library
153(1)
9.3 Some examples
154(9)
9.3.1 Market basket analysis
154(1)
9.3.2 PBX system risk analysis
155(5)
9.3.3 A bank's operational risk analysis
160(3)
9.4 Summary
163(3)
References
166(3)
PART IV OPERATIONAL RISK APPLICATIONS AND INTEGRATION WITH OTHER DISCIPLINES
169(112)
10 Operational risk management beyond AMA: new ways to quantify non-recorded losses
171(28)
Giorgio Aprile
Antonio Pippi
Stefano Visinoni
10.1 Introduction
171(3)
10.1.1 The near miss and opportunity loss project
171(1)
10.1.2 The `near miss/opportunity loss' service
172(1)
10.1.3 Advantage to the user
173(1)
10.1.4 Outline of the chapter
173(1)
10.2 Non-recorded losses in a banking context
174(3)
10.2.1 Opportunity losses
174(1)
10.2.2 Near misses
175(2)
10.2.3 Multiple losses
177(1)
10.3 Methodology
177(7)
10.3.1 Measure the non-measured
177(1)
10.3.2 IT events vs. operational loss classes
178(2)
10.3.3 Quantification of opportunity losses: likelihood estimates
180(1)
10.3.4 Quantification of near misses: loss approach level
181(3)
10.3.5 Reconnection of multiple losses
184(1)
10.4 Performing the analysis: a case study
184(11)
10.4.1 Data availability: source databases
184(2)
10.4.2 IT OpR ontology
186(1)
10.4.3 Critical path of IT events: Bayesian networks
187(2)
10.4.4 Steps of the analysis
189(5)
10.4.5 Outputs of the service
194(1)
10.5 Summary
195(1)
References
196(3)
11 Combining operational risks in financial risk assessment scores
199(16)
Michael Munsch
Silvia Rohe
Monika Jungemann-Dorner
11.1 Interrelations between financial risk management and operational risk management
199(1)
11.2 Financial rating systems and scoring systems
200(2)
11.3 Data management for rating and scoring
202(2)
11.4 Use case: business retail ratings for assessment of probabilities of default
204(4)
11.5 Use case: quantitative financial ratings and prediction of fraud
208(2)
11.6 Use case: money laundering and identification of the beneficial owner
210(3)
11.7 Summary
213(1)
References
214(1)
12 Intelligent regulatory compliance
215(24)
Marcus Spies
Rolf Gubser
Markus Schacher
12.1 Introduction to standards and specifications for business governance
215(2)
12.2 Specifications for implementing a framework for business governance
217(5)
12.2.1 Business motivation model
218(1)
12.2.2 Semantics of business vocabulary and business rules
219(3)
12.3 Operational risk from a BMM/SBVR perspective
222(3)
12.4 Intelligent regulatory compliance based on BMM and SBVR
225(7)
12.4.1 Assessing influencers
227(1)
12.4.2 Identify risks and potential rewards
227(2)
12.4.3 Develop risk strategies
229(1)
12.4.4 Implement risk strategy
229(1)
12.4.5 Outlook: build adaptive IT systems
229(3)
12.5 Generalization: capturing essential concepts of operational risk in UML and BMM
232(4)
12.6 Summary
236(1)
References
237(2)
13 Democratisation of enterprise risk management
239(14)
Paolo Lombardi
Salvatore Piscuoglio
Ron S. Kenett
Yossi Raanan
Markus Lankinen
13.1 Democratisation of advanced risk management services
239(1)
13.2 Semantic-based technologies and enterprise-wide risk management
240(3)
13.3 An enterprise-wide risk management vision
243(2)
13.4 Integrated risk self-assessment: a service to attract customers
245(4)
13.5 A real-life example in the telecommunications industry
249(1)
13.6 Summary
250(1)
References
251(2)
14 Operational risks, quality, accidents and incidents
253(28)
Ron S. Kenett
Yossi Raanan
14.1 The convergence of risk and quality management
253(3)
14.2 Risks and the Taleb quadrants
256(2)
14.3 The quality ladder
258(4)
14.4 Risks, accidents and incidents
262(2)
14.5 Operational risks in the oil and gas industry
264(8)
14.6 Operational risks: data management, modelling and decision making
272(1)
14.7 Summary
273(1)
References
274(7)
Index 281
Ron S Kenett, Chairman and CEO of KPA Ltd. also Research Professor, University of Turin, Italy and International Professor, NYU School of Engineering, New York, USA.

Yossi Raanan, Senior Consultant at KPA Ltd. Also Senior Lecturer, Business School of the College of Management, Academic Studies, Rishon, LeZion, Israel.