Beschreibung Operational Risk Management: A Practical Approach to Intelligent Data Analysis (Statistics in Practice Book 106) (English Edition). 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.
Operational Risk Management: A Practical Approach to ~ Operational Risk Management: A Practical Approach to Intelligent Data Analysis - Ebook written by Ron S. Kenett, Yossi Raanan. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Operational Risk Management: A Practical Approach to Intelligent Data Analysis.
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Risk Management Framework for Information Systems and ~ cost-effective, risk management decisions about the systems supporting their missions and business functions; and incorporates security and privacy into the system development life cycle. Executing the RMF tasks links essential risk management processes at the system level to risk management process es at the organization level. In addition, it .
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Credit Risk Scorecards: Developing and Implementing ~ I would like to echo Harfouche's comments--it is a good book for junior modelers since it covers popular approaches in consumer credit risk modeling practice, from which I can tell Mr. Siddiqi is a very experienced modeler in industry, in the meanwhile it lacks of in-depth explanation and exploration for all the topics. Besides, when the author tries to dig deeper in some area, I soon realize .
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Bayesia Home ~ The BayesiaLab Workflow in Practice. Researchers can use BayesiaLab to encode their domain knowledge into a Bayesian network. Alternatively, BayesiaLab can machine-learn a network structure purely from data collected from the problem domain. Irrespective of the source, a Bayesian network becomes a representation of the underlying, often high .
Strength, Weakness, Opportunity, and Threat Analysis (SWOT ~ SWOT analysis is a technique for assessing the performance, competition, risk, and potential of a business, as well as part of a business such as a product line or division, an industry, or other .
Data Mining: How Companies Use Data to Find Useful ~ Data mining is a process used by companies to turn raw data into useful information by using software to look for patterns in large batches of data.
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