Event- And Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioner's Handbook

ISBN-13: 9781484274392 / Angielski / Miękka / 2022 / 1120 str.

Dr. Sudheesh Kumar Kattumannil

Opis

user level

Spis treści

Chapter-1 Commercial Banks, Banking Systems & Basel Recommendations

1.1 Introduction

1.2 Financial markets

1.3 Commercial Bank - Lines of Business and Products

1.4 Source Systems

1.5 Evolution of Basel Risk Management Recommendations

 

Chapter-2 Siloed Risk Management Systems

2.1 Introduction

2.2 Treasury’s Market Risk and Credit Risk Management

2.3 Credit Risk in the Loan Book

2.4 Asset Liability Management (ALM)

2.5 Anti-Money Laundering and Countering the Financing of Terrorism (AML-CFT).

2.6 Operational Risk Management (ORM)

 

Chapter-3 Enterprise Risk adjusted Return (ERRM) Model, Gap Analysis & Identification

3.1 Introduction

3.2 What caused the Siloed Architecture? What is the impact?

3.2.4 Integrated Risk Management & ERRM

3.3 Gap Identification

3.3.1 Document New Business Requirements

3.3.2 Review of ERRM Requirements

3.3.3 Define ERRM Conceptual Model

3.3.4 Review As-Is Operating Model

3.3.5 The Gap–What needs to be done?

3.4 Summary-Build & Improve Capabilities

 

Chapter-4 ERRM Methodology, High level Implementation Plan

4.1 Introduction

4.2 ERRM Methodology

 

Chapter-5 Enterprise Architecture

5.1 Introduction

5.2 Ontology-Driven Information Systems

5.3 Service-Orientated Architecture (SOA)

5.4 Microservices Architecture (MSA)

5.5 Introduction to Cloud, Data Virtualisation

5.6 Enterprise Event Driven Architecture

5.7 Enterprise Process Automation

5.8 Robotic Process Automation (RPA)

5.9 SOA-BPMS Convergence

5.10 Cost Management (CM)

5.11 Gap Resolutions – Enterprise Architecture category

 

Chapter-6 Enterprise Data Management

6.1 Introduction

6.2 Data Management Frameworks

6.3 Enterprise Data Management

6.4 Single View of the Truth

 

Chapter-7 Enterprise Risk Data Management

7.1 Introduction

7.2 Enterprise Risk Data Ontology

7.3 Ontology based ERRM System

7.4 Enterprise Risk_Return Data Strategy

7.5 Enterprise Risk Data Discovery

7.6 Event Driven, Data Centric Enterprise Risk Management

7.7 Risk Data Management Technology

7.8 Multidimensional Enterprise Risk Data Model

 

Chapter-8 Data Science and Enterprise Risk Return Management

8.1 Introduction

8.2 Maths & Stats in Risk Data Calculations

8.3 Theory and Concepts

8.4 Risk Management Models

8.5 Enterprise Risk-Return Model Governance

 

Chapter 9 Advanced Analytics and Knowledge Management

9.1 Introduction

9.2 Advanced Analytics

9.3 Knowledge Management, KM

9.5 Analytics Maturity Evaluation

 

Chapter-10 ERRM Capabilities & Improvements

10.1 Introduction

10.2 Enterprise Liquidity Management (ELM)

10.3 Dynamic ALM

10.4 Improved Risk Measures.

 

 

O autorze

Kannan Subramanian R is a Chartered Accountant with 35+ years of experience in the banking and financial services industry and has experience with financial markets in USA, Europe, and Asia. He has worked for Standard Chartered Bank and for leading banking solution companies, including the leading global risk management solution provider, Algorithmics (now part of IBM Risk Management & Analytics). He advises System Design Consulting Prospero AG on strategic matters and in the design of risk management and analytical solutions. He has successfully leveraged his academic and work experience in the area of banking, including risk management and banking automation.

Kannan's knowledge portals can be found at www.bankerrm.org and www.pborm.org.

Dr. Sudheesh Kumar Kattumannil is an Associate Professor at the Indian Statistical Institute in Chennai, India. His research interests include survival analysis, reliability theory, variance inequality, moment identity, estimation of income inequality measures, measurement error problems, and empirical likelihood inference. He has published on topics related to statistics, mathematics, and risk management. He is a recipient of the Jan Tinbergen Award for young statisticians (International Statistical Association, The Netherlands) as well as a recipient of an Indo-US fellowship. 

Blurb

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.

You will:

• 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