Operational Risk Management: A Practical Approach to Intelligent Data Analysis (Statistics in Practice)
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.
- 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.
Table of Contents
PART I INTRODUCTION TO OPERATIONAL RISK MANAGEMENT 1
1 Risk management: a general view 3
2 Operational risk management: an overview 19
PART II DATA FOR OPERATIONAL RISK MANAGEMENT AND ITS HANDLING 39
3 Ontology-based modelling and reasoning in operational risks 41
4 Semantic analysis of textual input 61
5 A case study of ETL for operational risks 79
6 Risk-based testing of web services 99
PART III OPERATIONAL RISK ANALYTICS 125
7 Scoring models for operational risks 127
8 Bayesian merging and calibration for operational risks 137
9 Measures of association applied to operational risks 149
PART IV OPERATIONAL RISK APPLICATIONS AND INTEGRATION WITH OTHER DISCIPLINES 169
10 Operational risk management beyond AMA: new ways to quantify non-recorded losses 171
11 Combining operational risks in financial risk assessment scores 199
12 Intelligent regulatory compliance Marcus Spies, Rolf Gubser and Markus Schacher 215
13 Democratisation of enterprise risk management 239
14 Operational risks, quality, accidents and incidents 253