Models and methods for operational risks assessment and mitigationare gaining importance in financial institutions, healthcareorganizations, industry, businesses and organisations in general.This book introduces modern Operational Risk Management anddescribes how various data sources of different types, both numericand semantic sources such as text can be integrated and analyzed.The book also demonstrates how Operational Risk Management issynergetic to other risk management activities such as FinancialRisk Management and Safety Management.
Operational Risk Management: a practical approach tointelligent data analysis provides practical and testedmethodologies for combining structured and unstructured,semantic-based data, and numeric data, in Operational RiskManagement (OpR) data analysis.
- The book is presented in four parts: 1) Introduction to OpRManagement, 2) Data for OpR Management, 3) OpR Analytics and 4) OpRApplications and its Integration with other Disciplines.
- Explores integration of semantic, unstructured textual data, inOperational Risk Management.
- Provides novel techniques for combining qualitative andquantitative information to assess risks and design mitigationstrategies.
- Presents a comprehensive treatment of "near-misses" data andincidents in Operational Risk Management.
- Looks at case studies in the financial and industrialsector.
- Discusses application of ontology engineering to modelknowledge used in Operational Risk Management.
Many real life examples are presented, mostly based on theMUSING project co-funded by the EU FP6 Information SocietyTechnology Programme. It provides a unique multidisciplinaryperspective on the important and evolving topic of Operational RiskManagement. The book will be useful to operational riskpractitioners, risk managers in banks, hospitals and industrylooking for modern approaches to risk management that combine ananalysis of structured and unstructured data. The book will alsobenefit academics interested in research in this field, looking fortechniques developed in response to real world problems.
About the Author
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.
Table of Contents
Notes on Contributors.
List of Acronyms.
PART I INTRODUCTION TO OPERATIONAL RISK MANAGEMENT.
1 Risk management: a general view (Ron S. Kenett,Richard Pike and Yossi Raanan).
1.2 Definitions of risk.
1.3 Impact of risk.
1.4 Types of risk.
1.5 Enterprise risk management.
1.6 State of the art in enterprise risk management.
2 Operational risk management: an overview (YossiRaanan, Ron S. Kenett and Richard Pike).
2.2 Definitions of operational risk management.
2.3 Operational risk management techniques.
2.4 Operational risk statistical models.
2.5 Operational risk measurement techniques.
PART II DATA FOR OPERATIONAL RISK MANAGEMENT AND ITSHANDLING.
3 Ontology-based modelling and reasoning in operationalrisks (Christian Leibold, Hans-Ulrich Krieger andMarcus Spies).
3.2 Generic and axiomatic ontologies.
3.3 Domain-independent ontologies.
3.4 Standard reference ontologies.
3.5 Operational risk management.
4 Semantic analysis of textual input (HoracioSaggion, Thierry Declerck and Kalina Bontcheva).
4.2 Information extraction.
4.3 The general architecture for text engineering.
4.4 Text analysis components.
4.5 Ontology support.
4.6 Ontology-based information extraction.
5 A case study of ETL for operationalrisks (Valerio Grossi and Andrea Romei).
5.2 ETL (Extract, Transform and Load).
5.3 Case study specification.
5.4 The ETL-based solution.
6 Risk-based testing of web services (XiaoyingBai and Ron S. Kenett).
6.3 Problem statement.
6.4 Risk assessment.
6.5 Risk-based adaptive group testing.
PART III OPERATIONAL RISK ANALYTICS.
7 Scoring models for operational risks (PaoloGiudici).
7.2 Actuarial methods.
7.3 Scorecard models.
7.4 Integrated scorecard models.
8 Bayesian merging and calibration for operationalrisks (Silvia Figini).
8.2 Methodological proposal.
9 Measures of association applied to operationalrisks (Ron S. Kenett and Silvia Salini).
9.2 The arules R script library.
9.3 Some examples.
PART IV OPERATIONAL RISK APPLICATIONS AND INTEGRATION WITHOTHER DISCIPLINES.
10 Operational risk management beyond AMA: new ways toquantify non-recorded losses (Giorgio Aprile, AntonioPippi and Stefano Visinoni).
10.2 Non-recorded losses in a banking context.
10.4 Performing the analysis: a case study.
11 Combining operational risks in financial risk assessmentscores (Michael Munsch, Silvia Rohe and MonikaJungemann-Dorner).
11.1 Interrelations between financial risk management andoperational risk management.
11.2 Financial rating systems and scoring systems.
11.3 Data management for rating and scoring.
11.4 Use case: business retail ratings for assessment ofprobabilities of default.
11.5 Use case: quantitative financial ratings and prediction offraud.
11.6 Use case: money laundering and identification of thebeneficial owner.
12 Intelligent regulatory compliance (MarcusSpies, Rolf Gubser and Markus Schacher).
12.1 Introduction to standards and specifications for businessgovernance.
12.2 Specifications for implementing a framework for businessgovernance.
12.3 Operational risk from a BMM/SBVR perspective.
12.4 Intelligent regulatory compliance based on BMM andSBVR.
12.5 Generalization: capturing essential concepts of operationalrisk in UML and BMM.
13 Democratisation of enterprise riskmanagement (Paolo Lombardi, Salvatore Piscuoglio, RonS. Kenett, Yossi Raanan and Markus Lankinen).
13.1 Democratisation of advanced risk management services.
13.2 Semantic-based technologies and enterprise-wide riskmanagement.
13.3 An enterprise-wide risk management vision.
13.4 Integrated risk self-assessment: a service to attractcustomers.
13.5 A real-life example in the telecommunications industry.
14 Operational risks, quality, accidents andincidents (Ron S. Kenett and Yossi Raanan).
14.1 The convergence of risk and quality management.
14.2 Risks and the Taleb quadrants.
14.3 The quality ladder.
14.4 Risks, accidents and incidents.
14.5 Operational risks in the oil and gas industry.
14.6 Operational risks: data management, modelling and decisionmaking.