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Event- and Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioners Handbook: Event- and Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioners Handbook
: Kannan Subramanian R., Dr. Sudheesh Kumar Kattumannil
: Apress
: 2022
: 1112
: pdf (true)
: 72.2 MB

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.

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. The main constraint with ML is that it is widely perceived as a black box approach. Many risk auditors have opined that the audit of these processing environments is difficult as there is limited information in the logs.

Banks are beginning to move in the direction of managing their data as a science, realizing that the approach can support them in improving risk-adjusted returns. Bank management staff have to use their human capital to build and manage their enterprise knowledge management. Banks should consider the information technology team data providers. Data scientists play an important role in providing and improving risk-adjusted return methods, models, and techniques. The following factors have also increased the criticality of managing data: (a) competition from tech-savvy players; (b) increasing usage of financial derivatives for trading; (c) increased lending in the areas of project finance; (d) the changing landscape of enterprise liquidity management; (e) the need for improved riskreturn measurement; and (f) continuing changes to the regulatory landscape.

As this book is not on quantitative methods, there is no hypothesis or proof submission on risk modeling. The statistical discussion could be elementary for risk quantification experts and has been included to:

- further elaborate the fundamental differences between accounting and risk data;
- highlight the differences between the double-entry accounting system concept and enterprise risk management, theory, and concepts; and
- highlight the nuances of data management in risk modeling.

The enterprise architect (services-based architecture), data architect (data as a service, virtualization, data lineage), and technology experts (graph database, in-memory computing) need to understand the fundamental nature of risk management, the fundamentals of risk-return modeling, and its impact on the technology and data infrastructure.

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.

1. Commercial Banks, Banking Systems, and Basel Recommendations
2. Siloed Risk Management Systems
3. ERRM Gap Analysis & Identification
4. ERR Model Implementation Methodology
5. Enterprise Architecture
6. Enterprise Data Management
7. Enterprise Risk Data Management (A Subset of Enterprise Data Management)
8. Data Science and Enterprise RiskReturn Management
9. Advanced Analytics and Knowledge Management
10. ERRM Capabilities & Improvements

Event- and Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioners Handbook


: Ingvar16 4-01-2022, 07:31 | |
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