@article{1443590,
      recid = {1443590},
      author = {R, Kannan Subramanian, and Kattumannil, Sudheesh Kumar,},
      title = {Event- and data-centric enterprise risk-adjusted return  management : a banking practitioner's handbook /},
      pages = {1 online resource (1112 pages)},
      abstract = {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},
      url = {http://library.usi.edu/record/1443590},
      doi = {https://doi.org/10.1007/978-1-4842-7440-8},
}