Event- and Data-Centric Enterprise Risk-Adjusted Return Management A Banking Practitioner{u2019}s Handbook

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. 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 Boots