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2022 | Book

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Event- and Data-Centric Enterprise Risk-Adjusted Return Management

A Banking Practitioner’s Handbook

Authors: Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil

Publisher: Apress

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft" , Springer Professional "Technik"

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About this book

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 impactImplement an enterprise risk-adjusted return model (ERRM)Choose enterprise architecture and technologyDefine a reference enterprise architectureUnderstand enterprise data management methodologyDefine and use an enterprise data ontology and taxonomyCreate a multi-dimensional enterprise risk data modelUnderstand the relevance of event-driven architecture from business generation and risk management perspectivesImplement 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.

Table of Contents

Frontmatter
Chapter 1. Commercial Banks, Banking Systems, and Basel Recommendations
Abstract
Commercial banks play a critical role as financial intermediaries and channel funds from savers to borrowers in an efficient manner. In doing so, banks are exposed to market, credit, liquidity, and operational risks. These risks should be proactively managed in a holistic manner with a focus on risk appetite and risk-adjusted returns. Members of banks’ upper management need an enterprise view of the risks so as to improve capital allocation decisions. During the last decade, with an objective of moving toward an enterprise risk-management model, banks have increased their investments in transforming their operating model.
Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil
Chapter 2. Siloed Risk Management Systems
Abstract
The as-is environment comprises the source systems explained in Section 1.3 of Chapter 1 and the risk management solutions explained in this chapter. The narrative in this chapter does not follow Basel III requirements, nor does it discuss quantitative risk modeling. There are numerous books on the quantitative modeling of each of the risk types. The objective of this chapter is to describe the different risk management solutions available to a commercial bank, the scope of the implementations, the data requirements for risk management, and the siloed nature of the solutions. In almost all banks, the risk data is fragmented and the environment is “band-aided.”
Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil
Chapter 3. ERRM Gap Analysis & Identification
Abstract
It is appropriate to use the term “enterprise risk–return management” (ERRM) rather than just enterprise risk management, as risks are inherent in any business activity, and a bank’s primary objectives are growth and profits. Basel’s BCBS 239 guidance on risk data aggregation can be effective only when a bank has an enterprise data model and governance. Many of the banks that have implemented or are implementing risk aggregation projects have not rectified their fractured architecture and fragmented data processing environment.
Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil
Chapter 4. ERR Model Implementation Methodology
Abstract
The upcoming chapters, 4 to 10, discuss how a bank can implement the ERRM model. The discussion guides the bank in selecting the relevant architecture and technology to build its ERRM capabilities. This book is for banks that buy software solutions. The bank needs to know its requirements, evaluate the vendor’s solution, and work with the vendor to make the ERR model implementation successful. Banks cannot live with limitations imposed by legacy issues or by their vendor solutions.
Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil
Chapter 5. Enterprise Architecture
Abstract
“Architecture” is defined by IEEE 1471 as “the fundamental organization of a system embodied in its components, their relationships to each other and to the environment and the principles guiding its design and evolution.” Using this definition, it can be stated that an architecture is a concept that is based on the relationship of its components. As risk and returns are intertwined, this book submits that enterprise risk ontology is an important aspect of the enterprise risk-adjusted return model and risk management system(s).
Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil
Chapter 6. Enterprise Data Management
Abstract
Accounting and risk data have common elements, but there are fundamental differences in their character and usage. This chapter explains enterprise data management by focusing on the data common to both the accounting and the risk management systems.
Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil
Chapter 7. Enterprise Risk Data Management (A Subset of Enterprise Data Management)
Abstract
The structure of this chapter is similar to that in the previous chapter on enterprise data management (EDM), but here the focus is on risk data.
Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil
Chapter 8. Data Science and Enterprise Risk–Return Management
Abstract
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.
Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil
Chapter 9. Advanced Analytics and Knowledge Management
Abstract
This chapter builds on the enterprise data management discussion from Chapters 6, 7, and 8. It covers the following topics:
Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil
Chapter 10. ERRM Capabilities & Improvements
Abstract
The benefits that a bank can derive from implementing the recommended capabilities are explained in Chapter 5 (“Enterprise Architecture”) and Chapter 9 (“Advanced Analytics and Knowledge Management”). This chapter wraps up the book by explaining the other significant benefits of implementing the enterprise risk-adjusted return model.
Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil
Backmatter
Metadata
Title
Event- and Data-Centric Enterprise Risk-Adjusted Return Management
Authors
Kannan Subramanian R
Dr. Sudheesh Kumar Kattumannil
Copyright Year
2022
Publisher
Apress
Electronic ISBN
978-1-4842-7440-8
Print ISBN
978-1-4842-7439-2
DOI
https://doi.org/10.1007/978-1-4842-7440-8

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