Event- and Data-Centric Enterprise Risk-Adjusted Return Management: A Banking Practitioner’s Handbook

Table of Contents
About the Authors
About the Technical Reviewer
Acknowledgments
Preface
Chapter 1: Commercial Banks, Banking Systems, and Basel Recommendations
	1.1 Financial Markets
		1.1.1 Currency Market (FX market, Forex market)
		1.1.2 Money Market
		1.1.3 Capital Market
		1.1.4 Commodities Market
		1.1.5 Exchange and the Over-the-Counter (OTC) Market
		Settlement
	1.2 Commercial Bank — Lines of Business and Products
		1.2.1 Treasury — The Hub of the Bank
			1.2.1.1 Foreign Exchange
				Cost of Carry
			1.2.1.2 Money Market
				Bonds
				Repurchase Agreement
					A Tri-party Repo
			1.2.1.3 Equity
				Options & Futures
			1.2.1.4 Commodity
				Commodity Options & Futures
					Commodity Swap
					Market Characteristics
				Post-trading Functions
				Risks Associated with Derivatives
			1.2.1.5 International Swaps and Derivatives Association (ISDA)
				Treasury Summarized Balance Sheet, P&L
		1.2.2 Corporate Banking
			1.2.2.1 Loans — Commercial Lending
			1.2.2.2 Small & Medium Enterprise Sector
			1.2.2.3 Specialized Lending
			1.2.2.4 Trade Finance
				Funded & Non-Funded Trade Finance Facilities
		1.2.3 Retail Banking
			1.2.3.1 Retail Liabilities
				Savings, Current Account, Time Deposits
					Deposit Insurance
					Safe Custody Service
			1.2.3.2 Retail Assets
				Retail Loans
			1.2.3.3 Private Banking/Wealth Management
				Business Delivery and Electronic Channels
					Branch Banking
					e-Channels
		1.2.4 Term Structure of Interest Rates (TSIR)
	1.3 Source Systems
		Introduction
		1.3.1 Specialized Systems
			1.3.1.1 Treasury
				Market Data
				Treasury Management System (TMS)
					Instrument Coverage across Modules
				Front, Middle, and Back Office
				The Modules
					The Key Features of the FX Module
					Exchange Position and Cash Position
					The Key Features of the MMKT Module
					Spreads
					Duration & Convexity
					Sensitivity Measurement – DV01, PV01, IE01
					Duration Hedge Ratio
					Convexity
					Equity Module
					Commodity Module
					Greeks and Risk Sensitivity
					Hedging with Derivatives
					Derivatives Trading
					Risk Attribution Analysis
			1.3.1.2 Lending
			1.3.1.3 Trade Finance
				Country Risk
				Money Laundering
				Bank Risk
				Fraud
		1.3.2 Core Banking System
		1.3.3 Domestic and International Payments
			Direct Payment using Payment Gateway
			Real-Time Gross Settlement (RTGS)
			SWIFT
		1.3.4 Systems Owned by Other Functions
			Sales & Marketing
			Finance
			Human Resources
			Premises (falls under Operations)
			Procurement (can be part of the Finance Division)
			Legal
			Governance, Risk & Compliance
			IT Governance System (falls under Operations)
		1.3.5 Other Systems
			1.3.5.1 Costing
			1.3.5.2 Funds Transfer Pricing (FTP)
				Funds Transfer Pricing Framework
				What Is Transfer Priced?
				The Transfer Pricing Curve
				Pricing Approaches
				Data Dimensions of FTP
				Funds Transfer Pricing System Implementation
				Adjustments in Transfer Pricing
				Efficient Product Pricing
				Profitability Management
	1.4 Evolution of Basel Risk Management Recommendations
		1.4.1 1988 Basel-I
			1996 Market Risk Amendment (1988 Accord amendment)
			First-Generation Credit Risk Management Models
		1.4.2 2004 Basel II
			Market Risk – Standardized Measurement Method6
			Operational Risk5
				Basic Indicator Approach5
				The Standardized Approach (TSA)5
				Advanced Measurement Approach (AMA)5
					Principles of Supervisory Review and Evaluation
				Basel 2.58
					Incremental Risk Charge – IRC9
		1.4.3 2010 Basel III
			Restricted the Leverage10
			An Overview of Liquidity Management under Basel III
				Net Stable Funding Ratio (NSFR)11
				Liquidity Coverage Ratio (LCR) Overview12
Chapter 2: Siloed Risk Management Systems
	Common Functions in Risk Management Systems
	2.1 Treasury’s Market Risk and Credit Risk Management
		2.1.1 Treasury Risk Management System Modules
			Modules in the System (Market & Credit Risk)
			2.1.1.1 Data Required
			2.1.1.2 Financial Engineering – Modeling Specification/Configuration
				2.1.1.2a Product-Model Specification
					Instrument Modeling
				2.1.1.2b Curve Specifications
					Overview of Different Types of Curves
			Curve Data
				Bootstrapping Curves
					Missing Market Data
					Calibration
				2.1.1.2c Portfolio Modeling
					Linear Portfolio
					Simulation
		2.1.2 Credit Risk in Treasury Books
			2.1.2.1 Data Specific to Treasury’s Credit Risk Exposure
			2.1.2.2 Financial Engineering – Modeling, Configuration
				2.1.2.2a Treasury Instruments Creating Credit Risk Exposure
				2.1.2.2b Credit Risk Curve
					Credit Value Adjustment (CVA) BCBS 325 & 424
				2.1.2.2c Credit Risk Modeling
		2.1.3 Treasury Market and Credit Risk Measurement
			2.1.3.1 Mark to Market (MtM)
			2.1.3.2 Sensitivity Analysis
				2.1.3.2a Template for Risk Measure Data
			2.1.3.3 Value at Risk (VaR)
				RiskMetrics,5
					Covariance Matrix4
				Scenario-based Monte Carlo Simulation4,5
					Scenario Generation
					Scenario Data
				Historical Simulation4,5
					Marginal VaR, Component VaR, Incremental VaR5
					Stressed VaR
					VaR Limitations
			2.1.3.4 Stress Testing
				Scenario Definition
					Scenario Types
				Configuring Stress Tests
					Scenario Sets
					Portfolio Selection
				5Market Risk Stress-Test Approach
					5Treasury – Credit Risk Stress Testing
			2.1.3.5 Credit Risk Reduction Techniques
				Credit Derivatives
					Credit Default Swaps (CDS)
		2.1.4 Performance Attribution
	2.2 Credit Risk in the Loan Book
		2.2.1 Risk Perspective of the Lending Process
			2.2.1.1 Internal Credit Rating System
				Obligor and Facility Rating
				Retail Lending – Individual
			2.2.1.2 Credit Monitoring
				Portfolio Composition
					Identifying Concentrations of Risk9
				Validate with External Rating
			2.2.1.3 Loan Book Stress Testing
			2.2.1.4 Credit Risk Management Approaches
				Definitions10
				Probability of Default (PD)10,11
					Probability of Default (PD) – Model Selection
				Recovery Rate (RR) 10,11
				Loss Given Default (LGD)10,11
					LGD Models
				Expected Loss (EL)10,11
				Exposure at Default (EAD)10,11
				Maturity (M)10,11
				Calculation Approaches for Credit VaR
					Unexpected Loss (UL)10,11
	2.3 Asset Liability Management (ALM)
		2.3.1 ALM Overview
			Central Bank Operations and Their Impact on a Bank’s ALM
			Commercial Bank ALM Objectives
		2.3.2 Multi-Currency ALM System
			Chart of Accounts & Aggregating Risk Positions
			Cash-Flow Modeling, Monitoring, Forecasting
		2.3.3 ALM Risks
			Causal Events for Liquidity Risk
			IRR Management
		2.3.4 ALM Metrics
			2.3.4.1 Ratio Analysis
			2.3.4.2 Funding Matrix
			2.3.4.3 Rate-Sensitivity Gap Analysis
				Implications
			2.3.4.4 Duration Gap (DGAP) Analysis
				Duration Gap Model
				Sensitivity of Economic Value of Equity (EVE)
					Economic Value of Equity
			2.3.4.5 Convexity
			2.3.4.6 Portfolio & Balance Sheet Immunization
				Balance Sheet Immunization
			2.3.4.7 Asset Liability Efficient Frontier (ALEF) Analysis
		2.3.5 Asset Liability Management Committee (ALCO)
			Risk Appetite Framework – ALM
			Data Perspectives for Net Interest Margin (NIM) Targeting
			IRR and NIM Management
				Data Perspectives for NIM Targeting
				Risk Limits and Controls
	2.4 Anti–Money Laundering and Countering the Financing of Terrorism (AML-CFT)
		International Effort for the Prevention and Detection of ML and FT
		ML-FT Risk Identification
		2.4.1 Risk Analysis and Assessment
			Root Cause Analysis
		2.4.2 Risk Mitigation, Control Corrections, and Improvement
		2.4.3 Testing of Corrective Action
		2.4.4 Residual Risk Monitoring
		2.4.5 The AML-CFT Solution
	2.5 Operational Risk Management (ORM)
		2.5.1 Risk and Control Self-Assessment (RCSA)
			Technology Division – RCSA Areas
		2.5.2 Operational Risk Case Studies
			2.5.2.1 Business Disruption
				Acts of God and Business Continuity Planning
					BCP Monitoring Procedure
			2.5.2.2 Data Compromise or Theft
				Data Compromise
			2.5.2.3 Fraud, Staff / Internal–External collusion
			2.5.2.4 Selling of Complex Products (Risk Culture)
			2.5.2.5 Outsourcing
		2.5.3 Risk Monitoring
			Early Warning Signals, KRI
		2.5.4 Corrective Action Planning (CAP)
		2.5.5 Loss Database Module
			2.5.5.1 Internal Data – Near Miss and Loss
			2.5.5.2 External Loss Data
		2.5.6 Economic Capital Calculation
	2.6 Siloed As-Is Risk Management Environment
Chapter 3: ERRM Gap Analysis & Identification
	3.1 What Caused the Siloed Architecture? What Is the Impact?
		3.1.1 Siloed Architecture
			3.1.1.1 Evolution of Banking
				Banking up to 1970
				Banking Between 1971 and 2000: Derivatives for Hedging
				Year 2001 Onwards: Derivatives Trading, Financial Innovation & Engineering
			3.1.1.2 Technology Evolution
				Electronic Data Processing Era
				Core Banking Era
				Present Digital Banking Era
			3.1.1.3 Risk Management Evolution
				The Third Driver
		3.1.2 Siloed Operating Model and Risk Management
			3.1.2.1 Organization Structure
				Operational Risk Management
			3.1.2.2 Siloed Risk Management Processes, Overlapping Functions
			3.1.2.3 Complex Environment Where Data Is a By-product
				Complex Banking Operating Environments (CBOE)
				Siloed Enterprise Architecture & Data Management
					Case Study – Complex Banking Operating Model
		3.1.3 BCBS 239 Is a Step Forward
		3.1.4 Integrated Risk Management & ERRM
			Integrated Risk Measurement – Risk Capital
	3.2 Gap Identification
		3.2.1 Review As-Is Operating Model
			Phase-1
			3.2.1.1 Treasury
				Treasury Management System
				Treasury – Middle Office
					Market Risk Management
					Model Review
				3.2.1.1.1 Treasury – Credit Risk
				Back Office: Books of Account
			3.2.1.2 Loan Book – Corporate & Retail
				Policy and Strategic Planning
				Corporate & Retail Lending – Front Office
				Corporate & Retail Lending – Middle Office
					Model Review
					Stress Testing
				Back Office
			3.2.1.3 Asset Liability Management
				Liquidity Risk Management
				Consolidated Management of Liquidity & IRR Risk
					Specific to Repricing and Optionality in Products
					Risk Mitigation Measures
			3.2.1.4 Funds Transfer Pricing
			3.2.1.5 Finance
				3.2.1.5.1 Enterprise Cost Allocation
				3.2.1.5.2 Review of Other Finance Department Issues
			3.2.1.6 Operations and Technology
				Information Technology Infrastructure
				Enterprise IT Governance
				Facilities Management
			3.2.1.7 Human Resources
			3.2.1.8 Legal Department
			3.2.1.9 Operational Risk Management
			3.2.1.10 Knowledge Management & Analytics
		3.2.2 Document New Business Requirements
			Phase 2
			3.2.2.1 Business Goals and Model
			3.2.2.2 Financial Inclusion
			3.2.2.3 SME Financing
			3.2.2.4 Omni-Channel Platform
			3.2.2.5 Wealth Management
			3.2.2.6 Improvements to Trade Financing Mechanism
				Supply Chain Financing
			3.2.2.7 Project Financing
				The Challenge
				Project Financing – Products
			3.2.2.8 Global Transaction Banking (GTB)
			3.2.2.9 Real-Time Treasury Management System
			3.2.2.10 Enterprise Liquidity Hub
			3.2.2.11 Activity-Based Costing (ABC) and Enterprise Cost Management
			3.2.2.12 Reference Data Management
			3.2.2.13 Customer Retention and Pricing
			3.2.2.14 Human Resources Automation
			3.2.2.15 Enterprise Resource Planning
			3.2.2.16 ERRM Controls
		3.2.3 Review of ERRM Requirements
			Phase 3
			3.2.3.1 Market Risk
				3.2.3.1.1 Financial Market Infrastructure (FMI)
				3.2.3.1.2 Fundamental Review of the Trading Book (FRTB)
				3.2.3.1.3 Standardized Approach (SA) and Simplified Standardized Approach (SSA)
				3.2.3.1.4 Internal Model Approach (IMA)5
					Back-testing5
				3.2.3.1.5 Interest Rate Risk in the Banking Book (IRRBB)
					BCBS 108 and BCBS 368
					BCBS 368
			3.2.3.2 Credit Risk
				Wrong-Way Risk
			3.2.3.3 Liquidity Risk
				3.2.3.3.1 BCBS 248 – Intra-day Liquidity
				3.2.3.3.2 Cash Flow at Risk
				3.2.3.3.3 Liquidity Coverage Ratio
					High-Quality Liquid Assets
					Collateral Management
					Liquidity Coverage Ratio (LCR) – Impact on Business Model
				3.2.3.3.4 Net Stable Funding Ratio
			3.2.3.4 Operational Risk Management
				The Business Indicator Component11 (BIC)
			3.2.3.5 Risks from New or Improved Business Requirement
			3.2.3.6 ERRM Framework – Performance Metrics
			3.2.3.7 Advanced Analytics and Enterprise Knowledge Management
		3.2.4 Define ERR Conceptual Model
			Phase 4
			3.2.4.1 Conceptual ERR Business Architecture
			3.2.4.2 Conceptual ERR Technical Architecture
		3.2.5 The Gap – What Needs to Be Done?
			Phase 5
			Gap 1 Business Requirements – New & Improvements
			Gap 2 Enterprise Architecture
				Gap 2.1 Services-based Enterprise Architecture
					Loosely Coupled, Interoperable, Scalable Banking Components
					Dynamic, Real-Time Treasury System
				Gap 2.2 Enterprise Liquidity Hub (ELH)
				Gap 2.3 Dynamic Asset Liability Management
				Gap 2.4 Open Banking Design (impact on enterprise architecture and data)
					Competition from Non-bank Entities
					Data Protection and Privacy
				Gap 2.5 Omni-Channel Platform
			Gap 3 Enterprise Data Management
				Gap 3.1 Enterprise Data Taxonomy and Ontology
				Gap 3.2 Single View of the Truth
				Gap 3.3 Real-Time Data Processing
				Gap 3.4 Data Democratization
				Gap 3.5 Data Gap & Enterprise Cost Allocation
				Gap 3.6 FRTB Data Challenge
				Gap 3.7 Data Management, P&L Reconciliation
				Gap 3.8 IRRBB Data Gap
				Gap 3.9 Reference Data
				Gap 3.10 Data Gaps in Lending Systems (Corporate & Retail)
					Identifying Concentrations of Risk
				Gap 3.11 Gaps for Bank-wide Stress Testing
				Gap 3.12 Timestamp
					Summary of As-Is Data Management Limitations
			Gap 4 Technology
				Gap 4.1 Process Automation
					Banking Process Automation using BPMS
				Gap 4.2 In-memory Computing
				Gap 4.3 Graph Database
				Gap 4.4 Big Data
				Gap 4.5 Streaming Data
				Gap 4.6 Focus on Data Flow, Provide Data as a Service
				Gap 4.7 Data Virtualization (DV)
				Gap 4.8 Bi-modal Capability
			Gap-5 Enterprise Risk-Adjusted Return management
				Gap 5.1 Improvement to Risk Measures Would Include
				Gap 5.2 Enterprise Control Framework
				Gap 5.3 Focus on Tail Behavior
				Gap 5.4 Copulas for Measuring Enterprise Risk
				Gap 5.5 Expected Shortfall (ES)
				Gap 5.6 Stress-Testing Framework
				Gap 5.7 Reverse Stress Testing
				Gap 5.8 Process-based Operational Risk Management
				Gap 5.9 Knowledge Management & Analytics
			Gap 6 Risk Culture, Organization Structure
	3.3 Summary – Build & Improve Capabilities
		Agile Bank of the Future Model
		Stop the Incremental Approach to Leveraging Technology
		Customer Experience
Chapter 4: ERR Model Implementation Methodology
	4.1 ERRM Methodology
		4.1.1 Project Governance
			ERRM Transformation Project – The Sponsor
			Steering Committee
			The Project Plan
		4.1.2 Corporate Governance
			4.1.2.1 Business Goals
			4.1.2.2 Organization Structure
		4.1.3 Enterprise Risk-Adjusted Return Governance
			4.1.3.1 Risk–Return Governance
				Stress Test
			4.1.3.2 Risk Appetite Framework (RAF)
				A Bank’s Risk Profile
				RAF and the Three Lines of Defense
				Annual Review and Continuous Improvement of RAF
			4.1.3.3 Risk Appetite Statement
				Obtain Executive Management and Board Approval
				Operationalize the RAS, Including Roles and Responsibilities
		4.1.4 Business Architecture (BA)
			4.1.4.1 Standardized Operating Model (SOM)
				Step 1 Finalize Changes
				Step 2 Standardize the Operating Model
				Step 3 Improve and Optimize
		4.1.5 Enterprise Architecture
		4.1.6 Enterprise Data Architecture & Management
			4.1.6.1 GDPR Compliance
			4.1.6.2 Data for Reporting
		4.1.7 Enterprise Costing Framework
		4.1.8 Enterprise Funds Transfer Pricing (FTP) Framework
		4.1.9 Revision of MR, CR, ALM, and ORM Frameworks
			4.1.9.1 Revised Market Risk Framework
			4.1.9.2 Revised Credit Risk Management Framework
			4.1.9.3 Revised Asset Liability Management Framework
				Liquidity Stress Testing
			4.1.9.4 Revised Operational Risk Management Framework
				Standards
		4.1.10 Enterprise Stress Testing
		4.1.11 Capital Adequacy
		4.1.12 Enterprise Knowledge Management (EKM)
			Customer Experience
			Centers of Excellence
Chapter 5: Enterprise Architecture
	5.1 Ontology-Driven Information Systems
		5.1.1 Core Principles of Enterprise Architecture
			Reusability, Simplicity, and Flexibility
			Value Creation
	5.2 Service-Orientated Architecture (SOA)
		5.2.1 Overview
			Elements of SOA
		5.2.2 Features of SOA
			Banking Industry Architecture Network (BIAN)2
		5.2.3 SOA Implementation
	5.3 Microservices Architecture (MSA)
		Case Studies
	5.4 Introduction to Cloud
		Case Study
	5.5 Enterprise Event–Driven Architecture
		5.5.1 Event–Driven Architecture (EDA) Overview
			Architecture & Technology4
				Event-Driven Implementation
			Events – Operations Management4
				Event Triggers
			EDA Governance
		5.5.2 Complex Event Processing (CEP)
			Examples of Event-Driven Applications
				Case Study: Apache Kafka
				Case Study: Rabobank – Business Event Bus
		5.5.3 COSO Model, Event-Driven Architecture & Process Automation
		5.5.4 Offensive & Defensive Events
			Time to Cause, Time to Impact, Time to Recover
			Fault Tree
			Event Streaming, TTI-TTC-TTR Application: Case Study
				TTI-TTC-TTR Explained using the 2007–08 Global Meltdown
					Time to Cause, 2004–2006
					The Causes of the 2007-08 Financial Crisis (TTC)
					Time to Impact, 2006–2008
					Time to Recover (TTR)
	5.6 Enterprise Process Automation
		5.6.1 Process-based Operating Model
			Banking Process Inventory
			Top-Down Approach
				Goal Roll Down to Process Level
				Enterprise Process Taxonomy & Process-based Risk Metrics
					Front-, Middle-, and Back-Office Functions
				What Constitutes a Good Process?
					Data & Risk Factors
					Risk Management
					Sub-processes
					“Called / Invoked Process”
		5.6.2 BPM Suite Components
			5.6.2.1 Business Process Modeling
			5.6.2.2 BPM Engine and Process Orchestration
			5.6.2.3 Intelligence and Rules Engine
			5.6.2.4 Enterprise Document/Content Management
			5.6.2.5 Business Activity Monitoring (BAM)
			5.6.2.6 Middleware – Enterprise Application Integration (EAI)
		5.6.3 Process Automation Examples
			Introduction
			5.6.3.1 Sales Processes – Four Examples
				High-Level Retail Sales Process (Asset/Liability)
					Data Capture of the Lead
				Sales Process for Personal Loan
				Sales Process for Home Loan
				Retail Liability Products
				Retail Sale of Investment Products
			5.6.3.2 Retail Banking (More Examples)
				International Funds Transfer by an Individual
				Retail – Dormant Account Activation
			5.6.3.3 Corporate Banking
				Corporate Long-Term Loan
					High Level – New Corporate Loan Account Process
				Customer Identification Program (Reusable Process)
				Corporate Long-Term Loan Appraisal
				Corporate Customer On-boarding
				Corporate Long-Term Loan Approval
				Corporate Long-Term Loan Disbursement
			Corporate Banking – Trade Finance
				Import LC Issuance
			5.6.3.4 Treasury Processes
				High-Level View of Hedging
				Treasury Process Automation Examples
				FX Forward Contract
					SWIFT Messages9
				Interest Rate Swap
					SWIFT Messages18
			5.6.3.5 Human Resources
				Bank Staff
					Staff & Role Profile Matching
				Staff Fraud
			5.6.3.6 IT Governance
			5.6.3.7 Risk Management Process
				High-Level Credit Monitoring Process
				Fraud and AML-CFT
				Liquidity and Solvency Risk
					Enterprise Liquidity Monitoring
			5.6.3.8 Risk Governance Process
				High-Level Independent Price Verification (IPV) Process
				Resolving Unexplained P&L
				Market Risk – Fundamental Review of the Trading Book Processes
					Risk Governance Non-Modellable (NM) Risk Factor (RF) – NMRF
				Credit Risk-PD Model Governance
					BPMS for Internal Risk Model Governance
				GDPR & Processes
		5.6.4 Process-based Operational Risk Management
			Risk Identification & Assessment
			Severity / Loss Estimation
			Control Assessment
			Corrective Action Testing & Approval
			Residual Risk
		5.6.5 Continuous Process Improvement
			Process Mining Based on Event & Process Logs, Simulation
				Process based Operating Model – Case studies
					Bank of America – Lean Six Sigma
					European Bank – SOA, BPMS (IBM case study)
					TD Banknorth – BPMS
	5.7 Robotic Process Automation (RPA)
		Risk Management and Robotic Process Automation
	5.8 SOA–BPMS Convergence
	5.9 Enterprise Cost Management
		Activity-Based Costing
			Cost of Controls
	5.10 Gap Resolutions – Enterprise Architecture Category
		5.10.1 Omni-Channel Platform
		5.10.2 Financial Inclusion
		5.10.3 Corporate Banking Improvements
			Supply-Chain Finance Solution
				Electronic Bill of Lading
			Trade Finance Solution – Vendors Collaborate
			Case Study – Banco Santander
Chapter 6: Enterprise Data Management
	6.1 Data Management Frameworks
		DAMA-DMBOK1
		DCAM2
		6.1.1 DAMA-DMBOK
		6.1.2 Data Management Capability Assessment Model
	6.2 Enterprise Data Management
		6.2.1 Data Taxonomy & Ontology
			6.2.1.1 Banking Business Glossary
				Standardized Data Definitions
				Glossary and Catalog
			6.2.1.2 Taxonomy & Ontology
				Taxonomy
					Data Owners
					European Network and Information Security Agency
				Ontology
					Knowledge Management & Ontology
					Ontology for a Commercial Bank
					Metamodel Ontology, Domain Ontology, and Instances
					Ontology Example - Funds Transfer Pricing (FTP)
			6.2.1.3 Semantic Web (SW) Technology
		6.2.2 Business Case for Enterprise Data Management
		6.2.3 Enterprise Data Management Strategy
			Focus on Data flow & Lineage, NOT Storage
			6.2.3.1 Real-Time Data Processing
			6.2.3.2 Alignment with Business Strategy
				Break the Silos
			6.2.3.3 Align Data Flows with Process Flows
				Process Automation & Data Lineage
					Align Data Flow with Process Flow
			6.2.3.4 Data as a Service
			6.2.3.5 Data Streaming (Good Fit for Real-Time Treasury Management)
			6.2.3.6 Data Ownership
			6.2.3.7 Data Sharing, Interoperability, and Reusability
			6.2.3.8 Centralized vs Decentralized
			6.2.3.9 Defensive and Offensive Data
			6.2.3.10 Data for Analytics and Knowledge Management
				Data Science
			6.2.3.11 Data Protection and Privacy
		6.2.4 Enterprise Data Model & Architecture
			6.2.4.1 Enterprise-wide Data Discovery
			6.2.4.2 Target Enterprise Data Model
				Data Models – Canonical & Logical
				Conceptual, Logical, Physical Models
				6.2.4.2.1 Master, Reference, Metadata, Transaction
					Types of Data
						Master Data Management
						Reference Data Management (RDM)
						Metadata
						Transaction Data4
						Lines of Business and Human Capital Data Models
				6.2.4.2.2 Enterprise IT Governance Data
			6.2.4.3 Data as a Service
			6.2.4.4 Data Streaming
			6.2.4.5 Lambda Architecture
			6.2.4.6 Kappa Architecture
			6.2.4.7 Protocols for Financial Messaging
				The Interactive Financial eXchange (IFX)
				The Financial Information Exchange protocol (FIX)
				An Example of Payment Infrastructure Security
		6.2.5 Enterprise Data Management Technology
			6.2.5.1 Enterprise Data Technology
				Data Virtualization (DV)
				Data Integration
				Data Abstraction9
				Federation vs Integration
				Big Data
			6.2.5.2 Database Management System (DBMS)
				NoSQL
					Graph Databases
						Building a Graph Database Model
				DBMS Comparison
					RDBMS, Hierarchical & Graph Database
				Data Warehouse
				Data Lakes
				Knowledge Graph
				Data Catalog
					Case Study for Data Catalog
			6.2.5.3 In-Memory Technology
				In-memory Databases (IMDB)
					Case Study – International Software Solution Vendor
		6.2.6 Data Management Program
			Data Management Phases
				Data Maintenance
				Data Synthesis
				Data Usage
				Data Publication
				Data Archival
				Data Purging
		6.2.7 Data Quality and Lineage
			6.2.7.1 Data Standard ≠ Data Quality
				Examples of Some Standards
					Financial Products Markup Language, ISO20022
					Payment Card Industry, Data Security Standards Council (PCI_DSS)
					Payment Security Directive_2
			6.2.7.2 Data Quality Framework
				Define a Data Quality Measurement Framework
				Legal and Institutional Environment
				Financing Accounting & Risk Computation – Standards & Compliance
				Accuracy and Reliability of Data
				Approach to Assessing Data Quality
					Three Data Quality Capabilities
				Method for Data Audit
				Integrity
			6.2.7.3 Data Lineage
				Data Lineage Analysis
				Data Lineage Dimensions
					Data-Item Relationships
				Event-Driven Architecture, BPMS, and Event Logs Establish Data Lineage
				Master Data & Metadata Quality Management
					Case Study
				Process Automation, Data Lineage, and Traceability
					The Third Checkpoint – Enterprise Risk-Adjusted Return Management
				Automated Data Lineage
		6.2.8 Data Control Environment
			6.2.8.1 Enterprise Data-Centric Security Model
				Derivation of Business Controls
				Derivation of Technical Controls
			6.2.8.2 Data Classification
				Accessibility to Data
			6.2.8.3 The GDPR Perspective
				Johari Window for Data Privacy
				Data Protection and Privacy Needs
					Open Banking
					Data-Sharing Models
					NIST & IAPP
			6.2.8.4 Data Transmission
				Security of Database Objects
			6.2.8.5 International Payments
				SWIFT – Mandatory Security Control Framework for Members
			6.2.8.6 Data Lineage and Algorithms
		6.2.9 Data Governance
			Data Governance Council (DGC)
			Enterprise Data Governance (EDG) Principles
			Enterprise Data Policy
			6.2.9.1 Master Data Governance (MDG)
				Chart of Account Classification
					Inputs for Charts of Accounts 21
				Enterprise Cost Allocation
				Funds Transfer Pricing
				Market, Credit, and Operational Risks
				Enterprise Liquidity Management
			6.2.9.2 Metadata Governance20
			6.2.9.3 Reference Data Governance20
	6.3 Reference ERRM Architecture
		6.3.1 Reference Enterprise Architecture for the ERR Model
			Event-Driven, Data-Centric, Process-Automated Enterprise Risk–Return Management
Chapter 7: Enterprise Risk Data Management (A Subset of Enterprise Data Management)
	7.1 Enterprise Risk Ontology
		7.1.1 Risk Data
			Risk Data Characteristics
			7.1.1.1 Scalar Data
			7.1.1.2 Numerical and Categorical Data
			7.1.1.3 Levels or Scales of Measurement
			7.1.1.4 Dimensionality
			7.1.1.5 Synchronous and Non-synchronous Data
			7.1.1.6 Curves and Data Requirements
				Yield Curve Data
				Basis Curve Data
					LIBOR Forward Curve Data
					Secured Overnight Financing Rate-Forward (SOFR) Curve Data
				Swap Curve
		7.1.2 Business Glossary
		7.1.3 ERRM Taxonomy
			ERRM Taxonomy, RAF & RAS
				Enterprise Risk and Performance Taxonomy
			7.1.3.1 Treasury Taxonomy
				ISDA Common Domain Model (CDM) Taxonomy1
			7.1.3.2 Credit Risk Management Taxonomy
			7.1.3.3 Liquidity Risk Management, ALM Taxonomy
				Interest Rate Risk in the Banking Book (IRRBB)2
			7.1.3.4 Operational Risk Management Taxonomy
				TARA Implementation Steps
				OCTAVE
			7.1.3.5 Stress-Testing Taxonomy
		7.1.4 Risk Data Dictionary
			Banking Data Ontology – Efforts by Stakeholders
				Time-Series Data Management
				Business Glossary & Data Dictionary
		7.1.5 Enterprise Risk-Adjusted Return Ontology
			Financial Industry Business Ontology (FIBO)
			7.1.5.1 Risk Data Classification
				Classification Impact of Risk Data on Financial Data
			7.1.5.2 The Ullman Triangle
			7.1.5.3 ERRM Ontology
				7.1.5.3.1 Market Risk – Examples
					Centralized Collateral Management Ontology
						Collateral Valuation
					Market Risk, Standardized Approach, Credit Value Adjustment Ontology
						Credit Value Adjustment (CVA)6
					Hedging Ontology
						Black Scholes Pricing (BSP) Simulation for Delta Hedging
						Dynamic Hedging8
					Ontology for Treasury – FIBO Case Study
				7.1.5.3.2 Credit Risk
				7.1.5.3.3 ALM Ontology
					High-Quality Liquid Assets (HQLAs)9
						Calculation9
					Centralized General Ledger
				7.1.5.3.4 Process-based Operational Risk Ontology
				7.1.5.3.5 Enterprise IT Governance
				7.1.5.3.6 Human Capital
	7.2 Ontology-based ERRM System
	7.3 Enterprise Risk–Return Data Strategy
		7.3.1 International Effort – Data Standardization
			7.3.1.1 ISDA’s Common Domain Model (CDM)10
			7.3.1.2 CPMI-IOSCO
				Timestamping
			7.3.1.3 LEI, ISIN
				Legal Entity Identifier, ISO 17442
				International Securities Identification Numbering system
			7.3.1.4 FIGI – Financial Instrument Global Identifier
			7.3.1.5 Data Governance Issues
		7.3.2 Enterprise Risk-adjusted Performance Metrics
			RAS and Early Warning Signal, KRIs, KPIs
		7.3.3 Event-Driven Offensive and Defensive Data Management
			Risk Data Strategy
	7.4 Enterprise Risk Data Discovery
		7.4.1 Risk Management Data Requirements
			Two Phases or Passes
			7.4.1.1 Product Risk
				Risks Inherent in Products
				Financial Instrument Characteristics
				Product Risk Classification (PRC)
					Simultaneous Checking of All Risk Types
			7.4.1.2 Customer Profile
			7.4.1.3 Backward Pass
				Banking Supervisor’s Statistical Data Warehouse
				Risk Profile and Risk-Weighted Assets
			7.4.1.4 Forward Pass – Product & Process Driven
				Market Risk
					Data-flow Diagram
					Entity-Relationship Diagram
					Case Study
						Barclays 2018 Project to Evaluate ISDA’s Common Domain Model (CDM)
							Conclusion
					Data Discovery – FRTB, IRRBB, CSRBB
					Data Classification11
						Sensitivity-based Standardized Approach31
						Default Risk Charge (DRC)11
						Residual Risk Add-on (RRAO)11
						Credit Value Adjustment (CVA)11
					Internal Model 13
						Classification of Risk Factors as Modellable 11
						Non-modellable11
						Ontology Aspect in Risk Factor Eligibility Test (RFET)12 Determination
						P&L Attribution Test (PLAT) 11
						CSRBB11
						IRRBB – Data Flow13
						IRRBB Governance
					Simplified Standardized Approach14
				Enterprise Credit Risk
					Counterparty Credit Risk (CCR)
					Lending
					Credit Risk Monitoring – Data Discovery
				ALM, Funding & Capital Adequacy
					Net Stable Funding Ratio (NSFR)16
					Common Equity Tiers17
				Operational Risk Management (ORM)18
					Internal Loss Data (ILD)
						Operational Risk Data Flow and Model
					External Loss Data (ELD)
					Business Environment and Internal Control Factors (BEICF)
					Scenario Analysis
		7.4.2 Master, Meta, Reference, Historic, Time-Series, Transaction Data
			7.4.2.1 The Approach
				Data Elements
			7.4.2.2 Risk Master Data Management (Risk MDM)
			7.4.2.3 RDM as a Service
				Reference Data Management (RDM)
			7.4.2.4 Risk Metadata Management
				Risk Dataset Template
				The Risk Catalog – Master, Meta, and Reference Data
			7.4.2.5 Historical Data
			7.4.2.6 Time-Series Data19
				Time-Series Data for MR, CR, and ALM
			7.4.2.7 Transaction Data, Risk Calculations
			7.4.2.8 Synthetic Data, Data Quality, and Data Lineage
		7.4.3 Enterprise Data Standardization
			Standardized Operating Model and Data Standardization
		7.4.4 Enterprise Risk Data Catalog
	7.5 Event-Driven, Data-Centric ERRM
		Event-Driven Architecture (EDA), Process Automation
		Event-Driven, Threat–Asset–Vulnerability (TAV) Approach
		7.5.1 Event Driven
			Event Triggers Business Activities
			7.5.1.1 Treasury
				Event Automating ISDA Documentation20
			7.5.1.2 Credit Risk Data Flow and Model
			7.5.1.3 Event-Driven, Data-Centric ALM
		7.5.2 Risk Register & Events
			Fault Tree Analysis (FTA)
				Cause–Risk–Consequence / Cause–Event–Consequence, Ontology Model
			Event Tree Analysis (ETA)
		7.5.3 State Transitions, Actions & Events
			State21
			Transition (can be positive or negative)21
			Action21
			Bank ATM21
			A Bank’s Internal Credit Rating System
				Risk Transmission
			7.5.3.1 Markov Chain
		7.5.4 Data State Transition Diagrams (DSTD)
			Advantages of Using State Diagrams
		7.5.5 Process Mining & State Transition
			Evidence-based BPM Minimizes Risks, Maximizes Returns
				Process Mining and Enterprise Liquidity Management
	7.6 Risk Data Management Technology
		7.6.1 Time-Series Database22
		7.6.2 In-memory Management and Graph Database Applications
			Event Trees and GraphDB23
			GraphDB for Lattice Structure
			Anti–Money Laundering, Countering the Financing of Terrorism
				Know Your Customer, Due Diligence, and Enhanced Due Diligence
				Complex Corporate Structures, Layered Identities
		7.6.3 C++, Python, R Programming
	7.7 Multi-dimensional Enterprise Risk Data Model
		7.7.1 Adaptation of Data Point Model for Enterprise Risk Data Model
			Multi-dimensional Enterprise Risk–Return Data Model
	7.8 Approach to Assessing EDM Maturity
		Major Components of the Data Governance Maturity Model
Chapter 8: Data Science and Enterprise Risk–Return Management
	8.1 Math & Stats in Risk Data Calculations
		Introduction to Different Disciplines of Mathematics and Statistics
			Linear algebra
				Example: Cholesky Decomposition
			Trigonometry
			Calculus
		8.1.1 Elementary Statistics
			Population and Sample
			Parameter and Statistic
			Variable, Observation and Random Variable
			8.1.1.1 Covariance
			8.1.1.2 Correlation
			8.1.1.3 Correlation Coefficients
				Pearson’s Correlation
				Spearman’s Rank Correlation (ρ)
				Kendall’s Rank Correlation (τ)
				Partial Correlation
			8.1.1.4 Bootstrapping Data
		8.1.2 Distributions
			8.1.2.1 Continuous Probability Distribution
				Normal Distribution
				Standard Normal Distribution
				Student’s T Distribution
				Chi-Square Distribution
				Exponential Distribution
					Properties of Exponential Distribution
				Pareto Distribution
				Log-Normal Distribution
				Weibull Distribution
			8.1.2.2 Fitting Loss Distributions
				Measure of the Goodness of Fit (AIC, BIC)
				Analyzing the Fit of Loss Distribution
				Exponential Distribution
				Weibull Distribution
				Pareto Distribution
				Gamma Distribution
				Log-normal Distribution
				Conclusion for This Example on Fitting Loss Distribution
			8.1.2.3 Discrete Probability Distributions
				Binomial Distribution
				Poisson Distribution
				Geometric Distribution
				Negative Binomial Distribution
			8.1.2.4 Selection of Data Distribution (e.g., for Operational Risk)
				Loss Frequency and Severity Distribution5
					Binomial Distribution
					Poisson Distribution
				Loss Severity Distribution
					Log-Normal Distribution
				Operational Loss Distribution
					Combining Loss Frequency with Loss Severity5
		8.1.3 Parametric Models and  Non-parametric Alternatives
			Parametric and Non-parametric Statistical Tests
			8.1.3.1 Z Test
			8.1.3.2 t-Test
			8.1.3.3 F-Test
			8.1.3.4 ANOVA (Analysis of Variance)
			8.1.3.5 Non-parametric Tests Used for Measuring Risks
				Comparison of Statistical Tests
		8.1.4 Discriminant Analysis
		8.1.5 Deterministic, Probabilistic, Stochastic Models
			Probabilistic or Stochastic Models
				Stochastic Process and Model
		8.1.6 Receiver Operating Characteristic (ROC) Curve
		8.1.7 Line of Equality, Concentration Measures
			Lorenz Curve, Gini Coefficient & Herfindahl–Hirschman Index
				Lorenz Curve
				Gini Coefficient8
					Credit & Market Risk Management
				Herfindahl–Hirschman Index (HHI)
		8.1.8 Regression Analysis
			8.1.8.1 Simple Linear Regression (SLR)
			8.1.8.2 Multiple Linear Regression (MLR)
				R2, Adjusted R2
				Durbin Watson
			8.1.8.3 Non-linear Regression
				Binary Logistic Regression
				Poisson Regression Analysis
				Probit Regression
				Smoothing Spline
				Confidence Interval (CI)
		8.1.9 Risk Management – Statistical Usage
		8.1.10 Data Bias
			Statistical Bias
	8.2 Theory and Concepts
		8.2.1 Uncertainty in Risk-Return
			8.2.1.1 Mean Reversion, Mean Reversion Indicator Set
		8.2.2 Portfolio Theory
			Portfolio Theories
			8.2.2.1 Random Walk Theory2
			8.2.2.2 Martingale
			8.2.2.3 No Arbitrage Hypothesis
			8.2.2.4 CAPM and APT
				Capital Asset Pricing Model (CAPM) with Arbitrage Pricing Theory (APT)
				Arbitrage Pricing Theory (APT)
			8.2.2.5 Dynamic Global Immunization Theorem (Uses Portfolio Duration)
		8.2.3 Risk-Neutral Pricing
		8.2.4 Probability Theory and Information Theory
			8.2.4.1 Frequentist vs. Bayesian Probability
			8.2.4.2 Bayesian Statistics
				Bayesian Inference
				Bayes’ Theorem
		8.2.5 Law of Large Numbers (LLN)
		8.2.6 The Central Limit Theorem (CLT)
		8.2.7 The Fourier Transform
		8.2.8 Euler Theorem and Allocation
		8.2.9 Markov Chain
		8.2.10 Factor Models
			Linear Factor Model
			Dynamic Factor Model
		8.2.11 Eigen Decomposition of the Covariance Matrix
		8.2.12 Stochastic Differential Equations (SDE)
			Stochastic Differential Equation (SDE) Taxonomy
		8.2.13 Brownian Bridges
			Stochastic Simulation of Interest Rate Paths
		8.2.14 Structural and Reduced Form Models
		8.2.15 Enterprise Cause–Event–Consequence Discovery
			Causal Analytics
				Event Log for Causal Analysis
		8.2.16 Causal Loops and TTC, TTI, and TTR
			8.2.16.1 Causal Loops
			8.2.16.2 Time to Cause, Time to Impact, Time to Recover
				Market Efficiency, Information, TTC & TTI
		8.2.17 Tail Behavior
			8.2.17.1 Extreme Value Theory (EVT)
				Block Maxima
				Generalized Extreme Value (GEV)
				Peak Over Threshold (POT)
			8.2.17.2 Expected Shortfall
		Theory, Concepts & Occam’s Razor Principle
	8.3 Risk Management Models
		8.3.1a Time-Series Models
			Model Fitting
		8.3.1b Correlation Model Taxonomy
		8.3.2 Market Risk (MR) Models
			8.3.2.1 Multi-factor Models
				Risk-Neutral Density Models
			8.3.2.2 Option-Adjusted Spread (OAS)
			8.3.2.3 Hull–White Tree – Term Structure
			8.3.2.4 Yield Curve Construction Models
			8.3.2.5 EMV Model for Portfolio Behavior
				Exogenous, Maturity, Vintage (EMV)
			8.3.2.6 Asset Allocation Models
				Markowitz and the Black–Litterman Model
			8.3.2.7 Interest Rate Models
				LIBOR Market Model (LMM)
				Vasicek & CIR
				Nelson and Siegel
				Nelson–Siegel–Svensson
				Heath, Jarrow & Morton
					Interest Rate Model – Evaluation Criteria
			8.3.2.8 Statistical Decomposition, Eigen Portfolios
		8.3.3 Credit Risk
			8.3.3.1 Loan Portfolio Optimization
			8.3.3.2 Survival Models (Credit Risk – Recovery)
			8.3.3.3 Probability of Default Model
				Default Analysis
		8.3.4 Asset Liability Management (ALM)
			8.3.4.1 Merton Model
			8.3.4.2 ALM Strategies
				Multi-period Stochastic Models
				Dynamic Financial Analysis (DFA) Model
				Non-maturity Deposits (NMD),32
				Behavioral Model for Retail Depositors
		8.3.5 Operational Risk
			8.3.5.1 PetriNets
	8.4 ERR Model Governance
		8.4.1 Statistical Information System
		8.4.2 ERR Modeling Ecosystem
			8.4.2.1 The Taxonomy of Risk Models
			8.4.2.2 Risk Modeling Ecosystem
		8.4.3 Risk–Return Model Management
			8.4.3.1 Policies and Procedures
			8.4.3.2 Model Design and Code (not applicable for vendor-supplied models)
				Market Risk
					Synthetic Data for Risk Management
					Model to Generate Synthetic Data
			8.4.3.3 Model Testing
			8.4.3.4 Model Testing Documentation
			8.4.3.5 Model Approval and Deployment
		8.4.4 Interconnected Models
		8.4.5 ERR Model Governance
			Sandbox Environment for Risk–Return Model Governance
			8.4.5.1 First Line of Defense
			8.4.5.2 Second Line of Defense
			8.4.5.3 Model Audit (Third Line of Defense)
Chapter 9: Advanced Analytics and Knowledge Management
	9.1 Advanced Analytics
		9.1.1 Descriptive, Prescriptive, Predictive, Discovery
			Descriptive Analysis
			Predictive Analysis
				Analytics Moves from Rule Based to Intelligence
				Event-based Process Orchestration
			Prescriptive Analytics
			Discovery Analytics
		9.1.2 Algorithm
		9.1.3 Machine Learning
			9.1.3.1 The Three Model Categories
				Supervised & Unsupervised Learning
					The Confusion Matrix
				Reinforcement Learning (RL)
				Machine Learning Model Creation & Usage
			9.1.3.2 Machine Learning – Models, Methods, Techniques
				Dimension Reduction Models
					Principal Components Analysis (PCA)
					Singular Value Decomposition (SVD)
					Independent Component Analysis (ICA)
					Neighborhood Component Analysis (NCA)
				Other Models, Methods, Techniques
					Classification and Regression Trees (CART)
					Ross Quinlan Decision Trees
					Gradient Boosting, Bayes Classifier
					Ensemble Methods in Machine Learning
						Understanding the Ensemble Method by Referring to Decision Trees
					Model-Based Reinforcement Learning (RL)
						State, Action, and Reward
						Markov Decision Process (MDP)
					Boosted Decision Tree Model
					K-Means, K-Medoid Clustering
					Dynamic Programming (DP)
					Genetic Programming
					Bayesian Optimization
			9.1.3.3 Pregel – Processing Large-Scale Graphs
		9.1.4 Neural Networks (NN)
			9.1.4.1 Self-Normalizing Neural Networks (SNN)
			9.1.4.2 Shallow Neural Networks
			9.1.4.3 Deep Neural Networks
			9.1.4.4 Backpropagation
			9.1.4.5 Perceptron
		9.1.5 Overfitting or Underfitting the Data
		9.1.6 Deep Learning
		9.1.7 Reference Advanced Analytics Functional Architecture
	9.2 Knowledge Management (KM)
		9.2.1 Ontology-driven Knowledge Management (KM)
		9.2.2 KM Methodology
			Identify, Acquire & Create6
			Harness – Store & Share6
			Harvest – Apply (KM Cubes) & Use6
			9.2.2.1 KM Work Breakdown Structure
			9.2.2.2 “How to KM” in an ERRM Context
				Know What
				Know Why
				Know How
				Facet Analysis
				Know Where
			9.2.2.3 Continuous Improvement
		9.2.3 Knowledge Graphs (KG)
			9.2.3.1 Knowledge Graphs and Machine Learning
				Describing New Relations using Machine Learning
				Connectedness
				Gap Resolutions
	9.3 KM and AA Applications
		9.3.1 Sales & Marketing
		9.3.2 Risk Profiles
		9.3.3 Behavioral Analytics – Customer & Staff
			Deep Learning with Keras Library to Predict Customer Churn
		9.3.4 360° View of Human Capital (Employee)
			9.3.4.1 Employee Capability Measurement
		9.3.5 360° View of Customer
			9.3.5.1 State Transition Model and Credit Risk
				Credit Migration
				Credit Card Accounts
			9.3.5.2 Customer Segmentation
			9.3.5.3 Machine Learning and Chatbots
			9.3.5.4 Gamification
			9.3.5.5 Customer Experience
		9.3.6 Transaction Analysis
			9.3.6.1 Natural Language Processing (NLP)
			9.3.6.2 Sentiment Analysis
			9.3.6.3 Learning Customer Interaction
		9.3.7 Enterprise Fraud Prevention
			Neural Network System for Fraud Prevention
			Bank Card Fraud Detection using ANN
			Credit Card Fraud Analytics & Knowledge Graph
		9.3.8 Anti–Money Laundering & Countering the Financing of Terrorism (AML-CFT)
			Risk Mitigation Priority Weights
		9.3.9 Treasury Trading & Deep Learning
			Regularization Algorithms
				LASSO & Ridge Regression Technique for Automatic Trading Advice
				Regularization Used for Proxy Hedging
			Scenario Trees and Interest Rate Path Approaches
				Scenario Tree
			Treasury – Big Data, Stream Computing & Machine Learning
		9.3.10 Enterprise Liquidity Management (ELM)
			Maximum Entropy Principle
			Using Behavioral Analytics for IRRBB, Liquidity Management
				Non-maturing Deposits
		9.3.11 Wealth Management
		9.3.12 Credit Risk Management
			Ensemble Learning Methods and Credit Risk Management
			K-Means Clustering
		9.3.13 Banking Operations
			9.3.13.1 Branch Performance using K-means Clustering
			9.3.13.2 IT Risk – Knowledge Management
				TARA, CRAMM, OCTAVE – KM Methods
		9.3.14 Enterprise Content Management (ECM)
		9.3.15 Banking Case Studies
			Case Studies
				I. JPMC
				II. BBVA
				III. Dutch Lender ING
				IV. Commonwealth Bank of Australia
				V. Triodos Bank
				VI. Synthetic Data
				VII. Algo-driven Non-deliverable Forward Trade Execution
				VIII. Banking Supervisor/Central Bank
			Summary
	9.4 Analytics Maturity Evaluation
		Data Analytics Maturity Phases
		Enterprise Knowledge Management
Chapter 10: ERRM Capabilities & Improvements
	10.1 Enterprise Liquidity Management (ELM)
		10.1.1 Liquidity Assessment Principles
		10.1.2 Basel III – Liquidity Risk Framework, Main2
		10.1.3 Implementing the New ELM
			10.1.3.1 Standardized Operating Model (SOM)
			10.1.3.2 Chart of Accounts
			10.1.3.3 Real-Time Payments
				RTGS
				SWIFT
					Nostro Management3
						Real-Time Nostro Management
					Liquidity Implementation Task Force (LITF)4
						SWIFT, LITF & Enterprise Liquidity Management
						SWIFT Instant Payment and Domestic Direct Payment5
						Back-Dated Entries and Forward Value–Dated Entries
						gpi Tracker7
			10.1.3.4 Treasury8 Centralized Real-Time Collateral Management
				Real-Time Collateral Management
				Dynamic Hedging
			10.1.3.5 Enterprise Liquidity Hub (ELH)
				Enterprise Liquidity Hub Design
				Unlocking the “Liquidity Trapped in Silos” Model
					Virtual ELH
					Physical ELH
				Liquidity Risk Event
				Normal & Stressed Cash Flows
					Cashflow Predictions using Machine Learning-Ensemble Prediction Model
					Liquidity Risk Simulation
					Earnings at Risk Model10
				Liquidity Management – TTC, TTI, and TTR
					Time to Cause
					Time to Impact – Multiple Shocks
					Time to Recover
				System Dynamics (SD) for ELM11
				The “Offensive Use” of the Enterprise Liquidity Hub (Events and Data)
					Global Transaction Banking (GTB)
		10.1.4 Liquidity Stress-Testing Framework
			Four Type of Shocks
				Enterprise Liquidity Stress Testing
				Liquidity Shortfall under Stress
			Stress Test Output – Corrective Action
			Liquidity Risk Tail Events
		10.1.5 Developing a Contingency Funding Plan (CFP)
			Stress Event Types and CFP
		10.1.6 Monitoring Intra-day Liquidity Risk14
			Risk Appetite Monitoring
			10.1.6.1 Internal Enterprise Asset Liquidity Index
			10.1.6.2 Funding Matrix
				Intra-Day Liquidity Controls
		10.1.7 Introduction to Cash Flow @ Risk
			Cash Flow at Risk (CFaR)15
	10.2 Dynamic ALM16
		Dynamic Sources & Dynamic Uses
			Case Study – Liquidity Risk Can Make Banks Insolvent
	10.3 Liquidity Transfer Pricing (LTP)
		10.3.1 LTP as Part of FTP
			Liquidity Buffer, Cost of Carry, and LTP
				Liquidity Cushion – Cost of Carry Allocated using LTP Metrics
			FTP and Enterprise Data Management
	10.4 Improvements to Balance Sheet Optimization21
		Common Equity Tier 121
			Additional Tier 1 Capital Preference Shares21
		Tier 2 Capital21
		10.4.1 Balance Sheet Projections
			Risk Weighted Assets (RWA) Optimization
			Management, ALCO, Risk Committees – Risk Appetite Framework & Statement
		10.4.2 An Illustrative Optimization Approach
			Bank Ratings
			Machine Learning, Deep Learning
	10.5 Improved Risk Measures
		10.5.1 Process & Operating Model Maturity
			Residual Process Risk
			Process Risk Score
			Process Maturity
			Process Risk Score and Process Maturity
				Process Maturity
				External Loss Calibration using Process Maturity Score
				Operating Model Maturity
		10.5.2 Liquidity-adjusted Market Risk
			Market Risk & Liquidity Risk
		10.5.3 Liquidity-adjusted Credit Risk
			Using Credit Default Swap (CDS) Bid–Ask
				Vector Auto-Regressive Model
				Cross-Correlation between Credit Risk and Liquidity Risk
		10.5.4 Risk-Adjusted NIM23
			The Holistic Balance Sheet View
		10.5.5 Tail Behavior
			Kurtosis
			Rate of Survival Function
			Regularly Varying Function
			Sum of Independent Random Variables
		10.5.6 Expected Shortfall / Conditional VaR
			Expected Shortfall under Regular Variation
			Maximum Domain of Attraction
			Estimation of ES
				Normal Distribution
				T Distribution
				Non-parametric Methods for Estimating Expected Shortfall
				Forecasts for Expected Shortfall
				Back-testing of Expected Shortfall
	10.6 Copulas for Measuring Enterprise Risk
	10.7 Bank-wide Stress Testing
		Bank-wide Stress-Testing Framework24
Conclusion
	C1. Enterprise Approach to Maximizing Risk-Adjusted Returns
		Risk Governance
		Single ERM Measure
		Expected Shortfall and Backtesting
		Risk-Adjusted NIM, Liquidity-Adjusted VaR
		Enterprise Stress Testing
		Risk-Weighted Asset (RWA) Optimization
	C2. Enterprise Architecture
		Ontology-based Information Systems
		Services and Micro-Services Architecture Orientation
		Event-Driven Architecture
	C3. Technology
		Robotics Process Automation (RPA)
	C4. Enterprise Data Management Technology
		Data Virtualization (DV) and Data as a  Service (DaaS)
		In-Memory Computing
		Graph Database and Knowledge Graphs
		Knowledge Management
		Machine Learning
		AI and ML Usage Challenges
		Sandbox – Modeling & Regulatory Needs
		PSD2 and GDPR
		Statutory Audit Qualification
		Polyglot Persistence
		Data Ops
	C5. Climate Change & Banking
		The Equator Principles
	C6. Data Is the Lifeblood of ERR Management
Appendix A: Abbreviations
Appendix B: List of Processes
Bibliography
	Chapter 1
	Chapter 2
	Chapter 3
	Chapter 4
	Chapter 5
	Chapter 6
	Chapter 7
	Chapter 8
	Chapter 9
	Chapter 10
Index