Operational Risk Management

Description

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.Content: Chapter 1 Risk Management: A General View (pages 1–18): Ron S. Kenett, Richard Pike and Yossi RaananChapter 2 Operational Risk Management: An Overview (pages 19–38): Yossi Raanan, Ron S. Kenett and Richard PikeChapter 3 Ontology‐Based Modelling and Reasoning in Operational Risks (pages 39–59): Christian Leibold, Hans‐Ulrich Krieger and Marcus SpiesChapter 4 Semantic Analysis of Textual Input (pages 61–78): Horacio Saggion, Thierry Declerck and Kalina BontchevaChapter 5 A Case Study of ETL for Operational Risks (pages 79–97): Valerio Grossi and Andrea RomeiChapter 6 Risk‐Based Testing of Web Services (pages 99–123): Xiaoying Bai and Ron S. KenettChapter 7 Scoring Models for Operational Risks (pages 125–135): Paolo GiudiciChapter 8 Bayesian Merging and Calibration for Operational Risks (pages 137–148): Silvia FiginiChapter 9 Measures of Association Applied to Operational Risks (pages 149–167): Ron S. Kenett and Silvia SaliniChapter 10 Operational Risk Management beyond AMA: New Ways to Quantify Non‐Recorded Losses (pages 169–197): Giorgio Aprile, Antonio Pippi and Stefano VisinoniChapter 11 Combining Operational Risks in Financial Risk Assessment Scores (pages 199–214): Michael Munsch, Silvia Rohe and Monika Jungemann‐DornerChapter 12 Intelligent Regulatory Compliance (pages 215–238): Marcus Spies, Rolf Gubser and Markus SchacherChapter 13 Democratisation of Enterprise Risk Management (pages 239–251): Paolo Lombardi, Salvatore Piscuoglio, Ron S. Ron S. Kenett Kenett, Yossi Raanan and Markus LankinenChapter 14 Operational Risks, Quality, Accidents and Incidents (pages 253–279): Ron S. Kenett and Yossi Raanan