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More About This Title Operational Risk Modeling in Financial Services -The Exposure, Occurrence, Impact Method
English
Transform your approach to oprisk modelling with a proven, non-statistical methodology
Operational Risk Modeling in Financial Services provides risk professionals with a forward-looking approach to risk modelling, based on structured management judgement over obsolete statistical methods. Proven over a decade’s use in significant banks and financial services firms in Europe and the US, the Exposure, Occurrence, Impact (XOI) method of operational risk modelling played an instrumental role in reshaping their oprisk modelling approaches; in this book, the expert team that developed this methodology offers practical, in-depth guidance on XOI use and applications for a variety of major risks.
The Basel Committee has dismissed statistical approaches to risk modelling, leaving regulators and practitioners searching for the next generation of oprisk quantification. The XOI method is ideally suited to fulfil this need, as a calculated, coordinated, consistent approach designed to bridge the gap between risk quantification and risk management. This book details the XOI framework and provides essential guidance for practitioners looking to change the oprisk modelling paradigm.
- Survey the range of current practices in operational risk analysis and modelling
- Track recent regulatory trends including capital modelling, stress testing and more
- Understand the XOI oprisk modelling method, and transition away from statistical approaches
- Apply XOI to major operational risks, such as disasters, fraud, conduct, legal and cyber risk
The financial services industry is in dire need of a new standard — a proven, transformational approach to operational risk that eliminates or mitigates the common issues with traditional approaches. Operational Risk Modeling in Financial Services provides practical, real-world guidance toward a more reliable methodology, shifting the conversation toward the future with a new kind of oprisk modelling.
English
PATRICK NAIM (left) is the CEO of Elseware and widely recognized as an expert for operational risk modeling and quantification. Patrick has extensive experience in advising banks, insurance and energy companies for over 20 years in Continental Europe, the United Kingdom, and North America. He is also the author of Risk Quantification: Management, DiagnosisandHedging and Bayesian Networks: a Practical Guide to Applications, both from Wiley.
LAURENT CONDAMIN (right), PHD, is Managing Partner and Researcher at Elseware. For the past 10 years, he has been advising the largest financial institutions. His areas of expertise are operational risk modeling, stress testing, credit rating modeling, project risk analysis, insurance coverage optimization and cost-benefit analysis.
English
List of Figures
List of Tables
Foreword
Preface
Part I: Lessons learned in 10 years of practice
Chapter 1: Creation of the method
1.1 From artificial intelligence to risk modelling
1.2 Model losses or risks?
Chapter 2: Introduction to the XOI method
2.1 A risk modeling doctrine
2.2 A knowledge management process
2.3 The eXposure - Occurrence - Impact (XOI) approach
2.4 The return of AI: Bayesian networks for risk assessment
Chapter 3: Lessons learned in 10 years of practice
3.1 Risk and Control Self-Assessment
3.2 Loss Data
3.3 Quantitative Models
3.4 Scenarios workshops
3.5 Correlations
3.6 Model validation
Part 2: Challenges of Operational Risk Measurement
Chapter 4: Definition and scope of operational risk
4.1. On risk taxonomies
4.2. Definition of Operational Risk
Chapter 5: The importance of operational risk
5.1. The importance of losses
5.2. The importance of operational risk capital
5.3. Adequacy of capital to losses
Chapter 6: The need for measurement
6.1. Regulatory requirements
6.2. Non-regulatory requirements
Chapter 7: The challenges of measurement
7.1. Introduction
7.2. Measuring risk or measuring risks?
7.3. Requirements of a risk measurement method
7.4. Risk measurement practices
Part 3: Practice of Operational Risk Measurement
Chapter 8: Risk and Control Self-Assessment
8.1 Introduction
8.2 Risk and control identification
8.3 Risk and control assessment
Chapter 9: Losses Modelling
9.1 Loss Distribution Approach
9.2 Loss Regression
Chapter 10: Scenario Analysis
10.1 Scope of scenario analysis
10.2 Scenario Identification
10.3 Scenario Assessment
Part 4: The Exposure, Occurrence, Impact Method
Chapter 11: An exposure-based model
11.1 A tsunami is not an unexpectedly big wave
11.2 Using available knowledge to inform risk analysis
11.3 Structured scenarios assessment
11.4 The XOI approach – Exposure, Occurrence an Impact
Chapter 12: Introduction to Bayesian networks
12.1 A bit of history
12.2 A bit of theory
12.3 Influence diagrams and decision theory
12.4 Introduction to inference in Bayesian networks
12.5 Introduction to learning in Bayesian networks
Chapter 13: Bayesian Networks for Risk Measurement
13.1 An example in car fleet management
Chapter 14: The XOI Methodology
14.1 Structure Design
14.2 Quantification
14.3 Simulation
Chapter 15: A scenario in internal fraud
15.1 Introduction
15.2 XOI Modelling
Chapter 16: A scenario in cyber risk
16.1 Definition
16.2 XOI Modelling
Chapter 17: A scenario in conduct risk
17.1 Definition
17.2 Types of misconduct
17.3 XOI Modelling
Chapter 18: Aggregation of scenarios
18.1 Introduction
18.2 Influence of a scenario on an environment factor
18.3 Influence of an environment factor on a scenario
18.4 Combining the influences
18.5 Turning the dependencies into correlations
Chapter 19: Applications
19.1 Introduction
19.2 Regulatory applications
19.3 Risk Management
Chapter 20: A step towards “Oprisk Metrics”
20.1 Introduction
20.2 Building Exposure Units Tables
20.3 Sources for driver quantification
20.4 Conclusion
