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Автор: Kannan Subramanian R., Dr. Sudheesh Kumar Kattumannil
Издательство: Apress
Год: 2022
Страниц: 1112
Язык: английский
Формат: pdf (true)
Размер: 72.2 MB
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. Data virtualization (DV) can unify both structured and unstructured data in real-time to power the ERR model. DV has grown in its usage and is a critical part of modern enterprise data architectures. Data as a service (DaaS) is a business-centric service that delivers data assets on demand using a standard connectivity protocol in a predetermined, configurable format and frequency. DV can be exploited by enabling DaaS for data democratization and decision making. Machine learning (ML) and deep learning algorithms are finding more use cases in risk management and in general banking functions. Banks with large derivative portfolios have been early adopters as ML is good at managing non-linear relationships between explanatory variables and explained variables. ML can improve accuracy as be applied for a broader set of variables.