Event- and data-centric enterprise risk-adjusted return management : a banking practitioner's handbook / Kannan Subramanian R, Sudheesh Kumar Kattumannil.
2022
HD61 .S83 2022
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Details
Title
Event- and data-centric enterprise risk-adjusted return management : a banking practitioner's handbook / Kannan Subramanian R, Sudheesh Kumar Kattumannil.
ISBN
9781484274408 (electronic bk.)
1484274407 (electronic bk.)
1484274393
9781484274392
1484274407 (electronic bk.)
1484274393
9781484274392
Published
[Berkeley, CA] : Apress, [2022]
Language
English
Description
1 online resource (1112 pages)
Other Standard Identifiers
10.1007/978-1-4842-7440-8 doi
Call Number
HD61 .S83 2022
Dewey Decimal Classification
658.155
Summary
Take a holistic view of enterprise risk-adjusted return management in banking. This book recommends that a bank transform its siloed operating model into an agile enterprise model. It offers an event-driven, process-based, data-centric approach to help banks plan and implement an enterprise risk-adjusted return model (ERRM), keeping the focus on business events, processes, and a loosely coupled enterprise service architecture. Most banks suffer from a lack of good quality data for risk-adjusted return management. This book provides an enterprise data management methodology that improves data quality by defining and using data ontology and taxonomy. It extends the data narrative with an explanation of the characteristics of risk data, the usage of machine learning, and provides an enterprise knowledge management methodology for risk-return optimization. The book provides numerous examples for process automation, data analytics, event management, knowledge management, and improvements to risk quantification. The book provides guidance on the underlying knowledge areas of banking, enterprise risk management, enterprise architecture, technology, event management, processes, and data science. The first part of the book explains the current state of banking architecture and its limitations. After defining a target model, it explains an approach to determine the "gap" and the second part of the book guides banks on how to implement the enterprise risk-adjusted return model. What You Will Learn Know what causes siloed architecture, and its impact Implement an enterprise risk-adjusted return model (ERRM) Choose enterprise architecture and technology Define a reference enterprise architecture Understand enterprise data management methodology Define and use an enterprise data ontology and taxonomy Create a multi-dimensional enterprise risk data model Understand the relevance of event-driven architecture from business generation and risk management perspectives Implement advanced analytics and knowledge management capabilities Who This Book Is For The global banking community, including: senior management of a bank, such as the Chief Risk Officer, Head of Treasury/Corporate Banking/Retail Banking, Chief Data Officer, and Chief Technology Officer. It is also relevant for banking software vendors, banking consultants, auditors, risk management consultants, banking supervisors, and government finance professionals
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Kattumannil, Sudheesh Kumar, author.
Available in Other Form
Event- and Data-Centric Enterprise Risk-Adjusted Return Management.
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Table of Contents
Chapter 1: Commercial Banks, Banking Systems, and Basel Recommendations
Chapter 2: Siloed Risk Management Systems
Chapter 3: Enterprise Risk Adjusted Return Model (ERRM), Gap Analysis, and Identification
Chapter 4: ERRM Methodology, High-level Implementation Plan
Chapter 5: Enterprise Architecture
Chapter 6: Enterprise Data Management
Chapter 7: Enterprise Risk Data Management
Chapter 8: Data Science and Enterprise Risk Return Management
Chapter 9: Advanced Analytics and Knowledge Management
Chapter 10: ERRM Capabilities and Improvements
Appendix A: Abbreviations
Appendix B. List of Processes.
Chapter 2: Siloed Risk Management Systems
Chapter 3: Enterprise Risk Adjusted Return Model (ERRM), Gap Analysis, and Identification
Chapter 4: ERRM Methodology, High-level Implementation Plan
Chapter 5: Enterprise Architecture
Chapter 6: Enterprise Data Management
Chapter 7: Enterprise Risk Data Management
Chapter 8: Data Science and Enterprise Risk Return Management
Chapter 9: Advanced Analytics and Knowledge Management
Chapter 10: ERRM Capabilities and Improvements
Appendix A: Abbreviations
Appendix B. List of Processes.