Event- and Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioner's Handboo
Kannan / Kumar Kattumannil Subramanian R
Résumé
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.
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.
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.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Apress |
| Auteur(s) | Kannan / Kumar Kattumannil Subramanian R |
| Parution | 17/01/2022 |
| Nb. de pages | 1090 |
| EAN13 | 9781484274392 |
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