Description
Book SynopsisTake 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 improveme
Table of ContentsChapter-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.
