Operational Risk Management. A Practical Approach To Intelligent Data Analysis

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Models and methods for operational risks assessment and mitigation are gaining importance in financial institutions, healthcare organizations, industry, businesses and organisations in general

Discusses application of ontology engineering to model knowledge used in Operational Risk Management

Explores integration of semantic, unstructured textual data, in Operational Risk Management

It provides a unique multidisciplinary perspective on the important and evolving topic of Operational Risk Management

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

Looks at case studies in the financial and industrial sector

Many real life examples are presented, mostly based on the MUSING project co-funded by the EU FP6 Information Society Technology Programme

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

Presents a comprehensive treatment of near-misses data and incidents in Operational Risk Management

Provides novel techniques for combining qualitative and quantitative information to assess risks and design mitigation strategies

The book also demonstrates how Operational Risk Management is synergetic to other risk management activities such as Financial Risk Management and Safety Management

The book will also benefit academics interested in research in this field, looking for techniques developed in response to real world problems.

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

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