Details
Event- and Data-Centric Enterprise Risk-Adjusted Return Management
A Banking Practitioner's Handbook|
62,99 € |
|
| Verlag: | Apress |
| Format: | |
| Veröffentl.: | 03.01.2022 |
| ISBN/EAN: | 9781484274408 |
| Sprache: | englisch |
| Anzahl Seiten: | 1090 |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<p>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.</p>
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.<br><p></p>
<p>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.</p><p><b>What You Will Learn</b></p>
<p></p><ul><li>Know what causes siloed architecture, and its impact</li><li>Implement an enterprise risk-adjusted return model (ERRM)</li><li>Choose enterprise architecture and technology</li><li>Define a reference enterprise architecture</li><li>Understand enterprise data management methodology</li><li>Define and use an enterprise data ontology and taxonomy</li><li>Create a multi-dimensional enterprise risk data model</li><li>Understand the relevance of event-driven architecture from business generation and risk management perspectives</li><li>Implement advanced analytics and knowledge management capabilities</li></ul><p><b><br></b></p><p><b>Who This Book Is For</b></p>
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.<p></p>
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.<br><p></p>
<p>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.</p><p><b>What You Will Learn</b></p>
<p></p><ul><li>Know what causes siloed architecture, and its impact</li><li>Implement an enterprise risk-adjusted return model (ERRM)</li><li>Choose enterprise architecture and technology</li><li>Define a reference enterprise architecture</li><li>Understand enterprise data management methodology</li><li>Define and use an enterprise data ontology and taxonomy</li><li>Create a multi-dimensional enterprise risk data model</li><li>Understand the relevance of event-driven architecture from business generation and risk management perspectives</li><li>Implement advanced analytics and knowledge management capabilities</li></ul><p><b><br></b></p><p><b>Who This Book Is For</b></p>
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.<p></p>
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.
<b>Kannan Subramanian R</b> is a Chartered Accountant with 35+ years of experience in the banking and financial services industry and has experience with financial markets in USA, Europe, and Asia. He has worked for Standard Chartered Bank and for leading banking solution companies, including the leading global risk management solution provider, Algorithmics (now part of IBM Risk Management & Analytics). He advises System Design Consulting Prospero AG on strategic matters and in the design of risk management and analytical solutions. He has successfully leveraged his academic and work experience in the area of banking, including risk management and banking automation.<div><br></div><div>Kannan's knowledge portals can be found at www.bankerrm.org and www.pborm.org.<div><br> <b>Dr. Sudheesh Kumar Kattumannil</b> is an Associate Professor at the Indian Statistical Institute in Chennai, India. His research interests include survival analysis, reliability theory, variance inequality, moment identity, estimation of income inequality measures, measurement error problems, and empirical likelihood inference. He has published on topics related to statistics, mathematics, and risk management. He is a recipient of the Jan Tinbergen Award for young statisticians (International Statistical Association, The Netherlands) as well as a recipient of an Indo-US fellowship. </div></div>
<p>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.</p><p>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.<br></p><p></p><p>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.</p><p>You will:</p><p></p><ul><li>Know what causes siloed architecture, and its impact</li><li>Implement an enterprise risk-adjusted return model (ERRM)</li><li>Choose enterprise architecture and technology</li><li>Define a reference enterprise architecture</li><li>Understand enterprise data management methodology</li><li>Define and use an enterprise data ontology and taxonomy</li><li>Create a multi-dimensional enterprise risk data model</li><li>Understand the relevance of event-driven architecture from business generation and risk management perspectives</li><li>Implement advanced analytics and knowledge management capabilities</li></ul>
Teaches you how to implement an enterprise risk-adjusted return model and resolve pain points in commercial banks Shows you how to review commercial banking processes, data flows, and risk calculations at a granular level Helps you gain advanced analytics and knowledge management capabilities
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