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Banks and banking. --- Agricultural banks --- Banking --- Banking industry --- Commercial banks --- Depository institutions --- Finance --- Financial institutions --- Money

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Banks and banking. --- Agricultural banks --- Banking --- Banking industry --- Commercial banks --- Depository institutions --- Finance --- Financial institutions --- Money

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This handbook provides an overview and analysis of state-of-the-art research in banking written by leading researchers in the field. It strikes a balance between abstract theory, empirical analysis, and practitioner and policy-related material.

Banks and banking --- Agricultural banks --- Banking --- Banking industry --- Commercial banks --- Depository institutions --- Finance --- Financial institutions --- Money --- E-books --- Banks and banking. --- Private finance

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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

Banks and banking. --- Financial risk management. --- Risk management --- Agricultural banks --- Banking --- Banking industry --- Commercial banks --- Depository institutions --- Finance --- Financial institutions --- Money --- Financial risk management --- Data processing. --- E-books

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• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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

Financial risk management. --- Financial risk management --- Banks and banking. --- Data processing. --- Risk management --- Agricultural banks --- Banking --- Banking industry --- Commercial banks --- Depository institutions --- Finance --- Financial institutions --- Money