Event- and Data-Centric Enterprise Risk-Adjusted Return Management (eBook)

A Banking Practitioner's Handbook

Dr. Sudheesh Kumar Kattumannil, Kannan Subramanian R (Autoren)

eBook Download: PDF

2022 | 1st ed.
XXVIII, 1090 Seiten
Apress (Verlag)
978-1-4842-7440-8 (ISBN)

Infos

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.

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.

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. 

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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 "e;gap"e; and the second part of the book guides banks on how to implement the enterprise risk-adjusted return model.What You Will LearnKnow what causes siloed architecture, and its impactImplement an enterprise risk-adjusted return model (ERRM)Choose enterprise architecture and technologyDefine a reference enterprise architectureUnderstand enterprise data management methodologyDefine and use an enterprise data ontology and taxonomyCreate a multi-dimensional enterprise risk data modelUnderstand the relevance of event-driven architecture from business generation and risk management perspectivesImplement advanced analytics and knowledge management capabilitiesWho This Book Is ForThe 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.

Inhalt

Table of Contents 4
About the Authors 16
About the Technical Reviewer 17
Acknowledgments 18
Preface 20
Chapter 1: Commercial Banks, Banking Systems, and Basel Recommendations 28
1.1 Financial Markets 29
1.1.1 Currency Market (FX market, Forex market) 29
1.1.2 Money Market 30
1.1.3 Capital Market 30
1.1.4 Commodities Market 30
1.1.5 Exchange and the Over-the-Counter (OTC) Market 30
Settlement 31
1.2 Commercial Bank — Lines of Business and Products 32
1.2.1 Treasury — The Hub of the Bank 33
1.2.1.1 Foreign Exchange 33
Cost of Carry 36
1.2.1.2 Money Market 36
Bonds 37
Repurchase Agreement 38
A Tri-party Repo 38
1.2.1.3 Equity 39
Options & Futures
1.2.1.4 Commodity 39
Commodity Options & Futures
Commodity Swap 40
Market Characteristics 40
Post-trading Functions 40
Risks Associated with Derivatives 41
1.2.1.5 International Swaps and Derivatives Association (ISDA) 42
Treasury Summarized Balance Sheet, P& L
1.2.2 Corporate Banking 45
1.2.2.1 Loans — Commercial Lending 46
1.2.2.2 Small & Medium Enterprise Sector
1.2.2.3 Specialized Lending 46
1.2.2.4 Trade Finance 47
Funded & Non-Funded Trade Finance Facilities
1.2.3 Retail Banking 48
1.2.3.1 Retail Liabilities 49
Savings, Current Account, Time Deposits 49
Deposit Insurance 49
Safe Custody Service 49
1.2.3.2 Retail Assets 50
Retail Loans 50
1.2.3.3 Private Banking/Wealth Management 51
Business Delivery and Electronic Channels 51
Branch Banking 51
e-Channels 52
1.2.4 Term Structure of Interest Rates (TSIR) 52
1.3 Source Systems 54
Introduction 54
1.3.1 Specialized Systems 56
1.3.1.1 Treasury 56
Market Data 56
Treasury Management System (TMS) 57
Instrument Coverage across Modules 58
Front, Middle, and Back Office 58
The Modules 59
The Key Features of the FX Module 59
Exchange Position and Cash Position 59
The Key Features of the MMKT Module 60
Spreads 61
Duration & Convexity
Sensitivity Measurement – DV01, PV01, IE01 64
Duration Hedge Ratio 65
Convexity 65
Equity Module 66
Commodity Module 66
Greeks and Risk Sensitivity 66
Hedging with Derivatives 68
Derivatives Trading 71
Risk Attribution Analysis 73
1.3.1.2 Lending 73
1.3.1.3 Trade Finance 74
Country Risk 74
Money Laundering 74
Bank Risk 74
Fraud 75
1.3.2 Core Banking System 75
1.3.3 Domestic and International Payments 76
Direct Payment using Payment Gateway 76
Real-Time Gross Settlement (RTGS) 76
SWIFT 76
1.3.4 Systems Owned by Other Functions 76
Sales & Marketing
Finance 77
Human Resources 77
Premises (falls under Operations) 77
Procurement (can be part of the Finance Division) 78
Legal 78
Governance, Risk & Compliance
IT Governance System (falls under Operations) 78
1.3.5 Other Systems 78
1.3.5.1 Costing 78
1.3.5.2 Funds Transfer Pricing (FTP) 79
Funds Transfer Pricing Framework 80
What Is Transfer Priced? 81
The Transfer Pricing Curve 81
Pricing Approaches 82
Data Dimensions of FTP 84
Funds Transfer Pricing System Implementation 85
Adjustments in Transfer Pricing 86
Efficient Product Pricing 87
Profitability Management 87
1.4 Evolution of Basel Risk Management Recommendations 88
1.4.1 1988 Basel-I 88
1996 Market Risk Amendment (1988 Accord amendment) 89
First-Generation Credit Risk Management Models 90
1.4.2 2004 Basel II 95
Market Risk – Standardized Measurement Method6 96
Operational Risk5 100
Basic Indicator Approach5 101
The Standardized Approach (TSA)5 102
Advanced Measurement Approach (AMA)5 103
Principles of Supervisory Review and Evaluation 104
Basel 2.58 105
Incremental Risk Charge – IRC9 105
1.4.3 2010 Basel III 105
Restricted the Leverage10 108
An Overview of Liquidity Management under Basel III 109
Net Stable Funding Ratio (NSFR)11 109
Liquidity Coverage Ratio (LCR) Overview12 110
Chapter 2: Siloed Risk Management Systems 112
Common Functions in Risk Management Systems 114
2.1 Treasury’s Market Risk and Credit Risk Management 116
2.1.1 Treasury Risk Management System Modules 116
Modules in the System (Market & Credit Risk)
2.1.1.1 Data Required 117
2.1.1.2 Financial Engineering – Modeling Specification/Configuration 119
2.1.1.2a Product-Model Specification 119
Instrument Modeling 119
2.1.1.2b Curve Specifications 124
Overview of Different Types of Curves 124
Curve Data 126
Bootstrapping Curves 127
Missing Market Data 128
Calibration 129
2.1.1.2c Portfolio Modeling 131
Linear Portfolio 132
Simulation 132
2.1.2 Credit Risk in Treasury Books 132
2.1.2.1 Data Specific to Treasury’s Credit Risk Exposure 133
2.1.2.2 Financial Engineering – Modeling, Configuration 133
2.1.2.2a Treasury Instruments Creating Credit Risk Exposure 133
2.1.2.2b Credit Risk Curve 134
Credit Value Adjustment (CVA) BCBS 325 & 424
2.1.2.2c Credit Risk Modeling 136
2.1.3 Treasury Market and Credit Risk Measurement 136
2.1.3.1 Mark to Market (MtM) 137
2.1.3.2 Sensitivity Analysis 137
2.1.3.2a Template for Risk Measure Data 138
2.1.3.3 Value at Risk (VaR) 138
RiskMetrics,5 140
Covariance Matrix4 140
Scenario-based Monte Carlo Simulation4,5 142
Scenario Generation 143
Scenario Data 145
Historical Simulation4,5 147
Marginal VaR, Component VaR, Incremental VaR5 148
Stressed VaR 149
VaR Limitations 151
2.1.3.4 Stress Testing 152
Scenario Definition 153
Scenario Types 153
Configuring Stress Tests 154
Scenario Sets 154
Portfolio Selection 154
5Market Risk Stress-Test Approach 155
5Treasury – Credit Risk Stress Testing 155
2.1.3.5 Credit Risk Reduction Techniques 156
Credit Derivatives 156
Credit Default Swaps (CDS) 157
2.1.4 Performance Attribution 157
2.2 Credit Risk in the Loan Book 159
2.2.1 Risk Perspective of the Lending Process 159
2.2.1.1 Internal Credit Rating System 160
Obligor and Facility Rating 161
Retail Lending – Individual 163
2.2.1.2 Credit Monitoring 163
Portfolio Composition 163
Identifying Concentrations of Risk9 163
Validate with External Rating 164
2.2.1.3 Loan Book Stress Testing 164
2.2.1.4 Credit Risk Management Approaches 165
Definitions10 166
Probability of Default (PD)10,11 167
Probability of Default (PD) – Model Selection 168
Recovery Rate (RR) 10,11 168
Loss Given Default (LGD)10,11 169
LGD Models 169
Expected Loss (EL)10,11 170
Exposure at Default (EAD)10,11 170
Maturity (M)10,11 171
Calculation Approaches for Credit VaR 172
Unexpected Loss (UL)10,11 172
2.3 Asset Liability Management (ALM) 174
2.3.1 ALM Overview 174
Central Bank Operations and Their Impact on a Bank’s ALM 174
Commercial Bank ALM Objectives 176
2.3.2 Multi-Currency ALM System 176
Chart of Accounts & Aggregating Risk Positions
Cash-Flow Modeling, Monitoring, Forecasting 179
2.3.3 ALM Risks 180
Causal Events for Liquidity Risk 181
IRR Management 182
2.3.4 ALM Metrics 183
2.3.4.1 Ratio Analysis 184
2.3.4.2 Funding Matrix 185
2.3.4.3 Rate-Sensitivity Gap Analysis 185
Implications 186
2.3.4.4 Duration Gap (DGAP) Analysis 186
Duration Gap Model 187
Sensitivity of Economic Value of Equity (EVE) 188
Economic Value of Equity 189
2.3.4.5 Convexity 189
2.3.4.6 Portfolio & Balance Sheet Immunization
Balance Sheet Immunization 193
2.3.4.7 Asset Liability Efficient Frontier (ALEF) Analysis 193
2.3.5 Asset Liability Management Committee (ALCO) 194
Risk Appetite Framework – ALM 194
Data Perspectives for Net Interest Margin (NIM) Targeting 195
IRR and NIM Management 196
Data Perspectives for NIM Targeting 197
Risk Limits and Controls 198
2.4 Anti–Money Laundering and Countering the Financing of Terrorism (AML-CFT) 198
International Effort for the Prevention and Detection of ML and FT 199
ML-FT Risk Identification 200
2.4.1 Risk Analysis and Assessment 201
Root Cause Analysis 202
2.4.2 Risk Mitigation, Control Corrections, and Improvement 203
2.4.3 Testing of Corrective Action 203
2.4.4 Residual Risk Monitoring 204
2.4.5 The AML-CFT Solution 204
2.5 Operational Risk Management (ORM) 205
2.5.1 Risk and Control Self-Assessment (RCSA) 207
Technology Division – RCSA Areas 209
2.5.2 Operational Risk Case Studies 209
2.5.2.1 Business Disruption 210
Acts of God and Business Continuity Planning 212
BCP Monitoring Procedure 212
2.5.2.2 Data Compromise or Theft 213
Data Compromise 214
2.5.2.3 Fraud, Staff / Internal–External collusion 217
2.5.2.4 Selling of Complex Products (Risk Culture) 222
2.5.2.5 Outsourcing 223
2.5.3 Risk Monitoring 225
Early Warning Signals, KRI 225
2.5.4 Corrective Action Planning (CAP) 226
2.5.5 Loss Database Module 227
2.5.5.1 Internal Data – Near Miss and Loss 228
2.5.5.2 External Loss Data 228
2.5.6 Economic Capital Calculation 229
2.6 Siloed As-Is Risk Management Environment 230
Chapter 3: ERRM Gap Analysis & Identification
3.1 What Caused the Siloed Architecture? What Is the Impact? 233
3.1.1 Siloed Architecture 233
3.1.1.1 Evolution of Banking 233
Banking up to 1970 233
Banking Between 1971 and 2000: Derivatives for Hedging 233
Year 2001 Onwards: Derivatives Trading, Financial Innovation & Engineering
3.1.1.2 Technology Evolution 234
Electronic Data Processing Era 234
Core Banking Era 235
Present Digital Banking Era 235
3.1.1.3 Risk Management Evolution 237
The Third Driver 237
3.1.2 Siloed Operating Model and Risk Management 237
3.1.2.1 Organization Structure 238
Operational Risk Management 239
3.1.2.2 Siloed Risk Management Processes, Overlapping Functions 240
3.1.2.3 Complex Environment Where Data Is a By-product 241
Complex Banking Operating Environments (CBOE) 241
Siloed Enterprise Architecture & Data Management
Case Study – Complex Banking Operating Model 243
3.1.3 BCBS 239 Is a Step Forward 244
3.1.4 Integrated Risk Management & ERRM
Integrated Risk Measurement – Risk Capital 246
3.2 Gap Identification 247
3.2.1 Review As-Is Operating Model 249
Phase-1 249
3.2.1.1 Treasury 250
Treasury Management System 250
Treasury – Middle Office 251
Market Risk Management 251
Model Review 252
3.2.1.1.1 Treasury – Credit Risk 252
Back Office: Books of Account 253
3.2.1.2 Loan Book – Corporate & Retail
Policy and Strategic Planning 253
Corporate & Retail Lending – Front Office
Corporate & Retail Lending – Middle Office
Model Review 255
Stress Testing 255
Back Office 256
3.2.1.3 Asset Liability Management 256
Liquidity Risk Management 257
Consolidated Management of Liquidity & IRR Risk
Specific to Repricing and Optionality in Products 259
Risk Mitigation Measures 259
3.2.1.4 Funds Transfer Pricing 260
3.2.1.5 Finance 260
3.2.1.5.1 Enterprise Cost Allocation 260
3.2.1.5.2 Review of Other Finance Department Issues 261
3.2.1.6 Operations and Technology 261
Information Technology Infrastructure 261
Enterprise IT Governance 263
Facilities Management 264
3.2.1.7 Human Resources 264
3.2.1.8 Legal Department 265
3.2.1.9 Operational Risk Management 265
3.2.1.10 Knowledge Management & Analytics
3.2.2 Document New Business Requirements 266
Phase 2 266
3.2.2.1 Business Goals and Model 266
3.2.2.2 Financial Inclusion 266
3.2.2.3 SME Financing 266
3.2.2.4 Omni-Channel Platform 267
3.2.2.5 Wealth Management 267
3.2.2.6 Improvements to Trade Financing Mechanism 268
Supply Chain Financing 268
3.2.2.7 Project Financing 269
The Challenge 269
Project Financing – Products 270
3.2.2.8 Global Transaction Banking (GTB) 270
3.2.2.9 Real-Time Treasury Management System 271
3.2.2.10 Enterprise Liquidity Hub 271
3.2.2.11 Activity-Based Costing (ABC) and Enterprise Cost Management 272
3.2.2.12 Reference Data Management 272
3.2.2.13 Customer Retention and Pricing 272
3.2.2.14 Human Resources Automation 273
3.2.2.15 Enterprise Resource Planning 273
3.2.2.16 ERRM Controls 274
3.2.3 Review of ERRM Requirements 274
Phase 3 275
3.2.3.1 Market Risk 275
3.2.3.1.1 Financial Market Infrastructure (FMI) 275
3.2.3.1.2 Fundamental Review of the Trading Book (FRTB) 276
3.2.3.1.3 Standardized Approach (SA) and Simplified Standardized Approach (SSA) 276
3.2.3.1.4 Internal Model Approach (IMA)5 277
Back-testing5 279
3.2.3.1.5 Interest Rate Risk in the Banking Book (IRRBB) 279
BCBS 108 and BCBS 368 279
BCBS 368 280
3.2.3.2 Credit Risk 283
Wrong-Way Risk 283
3.2.3.3 Liquidity Risk 283
3.2.3.3.1 BCBS 248 – Intra-day Liquidity 283
3.2.3.3.2 Cash Flow at Risk 285
3.2.3.3.3 Liquidity Coverage Ratio 285
High-Quality Liquid Assets 285
Collateral Management 286
Liquidity Coverage Ratio (LCR) – Impact on Business Model 286
3.2.3.3.4 Net Stable Funding Ratio 286
3.2.3.4 Operational Risk Management 287
The Business Indicator Component11 (BIC) 287
3.2.3.5 Risks from New or Improved Business Requirement 288
3.2.3.6 ERRM Framework – Performance Metrics 288
3.2.3.7 Advanced Analytics and Enterprise Knowledge Management 289
3.2.4 Define ERR Conceptual Model 290
Phase 4 290
3.2.4.1 Conceptual ERR Business Architecture 291
3.2.4.2 Conceptual ERR Technical Architecture 291
3.2.5 The Gap – What Needs to Be Done? 293
Phase 5 293
Gap 1 Business Requirements – New & Improvements
Gap 2 Enterprise Architecture 294
Gap 2.1 Services-based Enterprise Architecture 294
Loosely Coupled, Interoperable, Scalable Banking Components 294
Dynamic, Real-Time Treasury System 294
Gap 2.2 Enterprise Liquidity Hub (ELH) 295
Gap 2.3 Dynamic Asset Liability Management 296
Gap 2.4 Open Banking Design (impact on enterprise architecture and data) 296
Competition from Non-bank Entities 296
Data Protection and Privacy 296
Gap 2.5 Omni-Channel Platform 297
Gap 3 Enterprise Data Management 297
Gap 3.1 Enterprise Data Taxonomy and Ontology 297
Gap 3.2 Single View of the Truth 297
Gap 3.3 Real-Time Data Processing 297
Gap 3.4 Data Democratization 297
Gap 3.5 Data Gap & Enterprise Cost Allocation
Gap 3.6 FRTB Data Challenge 298
Gap 3.7 Data Management, P& L Reconciliation
Gap 3.8 IRRBB Data Gap 299
Gap 3.9 Reference Data 299
Gap 3.10 Data Gaps in Lending Systems (Corporate & Retail)
Identifying Concentrations of Risk 299
Gap 3.11 Gaps for Bank-wide Stress Testing 300
Gap 3.12 Timestamp 300
Summary of As-Is Data Management Limitations 300
Gap 4 Technology 301
Gap 4.1 Process Automation 301
Banking Process Automation using BPMS 301
Gap 4.2 In-memory Computing 301
Gap 4.3 Graph Database 301
Gap 4.4 Big Data 302
Gap 4.5 Streaming Data 302
Gap 4.6 Focus on Data Flow, Provide Data as a Service 302
Gap 4.7 Data Virtualization (DV) 302
Gap 4.8 Bi-modal Capability 302
Gap-5 Enterprise Risk-Adjusted Return management 303
Gap 5.1 Improvement to Risk Measures Would Include 303
Gap 5.2 Enterprise Control Framework 303
Gap 5.3 Focus on Tail Behavior 304
Gap 5.4 Copulas for Measuring Enterprise Risk 304
Gap 5.5 Expected Shortfall (ES) 304
Gap 5.6 Stress-Testing Framework 305
Gap 5.7 Reverse Stress Testing 305
Gap 5.8 Process-based Operational Risk Management 305
Gap 5.9 Knowledge Management & Analytics
Gap 6 Risk Culture, Organization Structure 307
3.3 Summary – Build & Improve Capabilities
Agile Bank of the Future Model 308
Stop the Incremental Approach to Leveraging Technology 309
Customer Experience 310
Chapter 4: ERR Model Implementation Methodology 311
4.1 ERRM Methodology 312
4.1.1 Project Governance 313
ERRM Transformation Project – The Sponsor 313
Steering Committee 313
The Project Plan 314
4.1.2 Corporate Governance 317
4.1.2.1 Business Goals 319
4.1.2.2 Organization Structure 321
4.1.3 Enterprise Risk-Adjusted Return Governance 322
4.1.3.1 Risk–Return Governance 322
Stress Test 328
4.1.3.2 Risk Appetite Framework (RAF) 328
A Bank’s Risk Profile 329
RAF and the Three Lines of Defense 331
Annual Review and Continuous Improvement of RAF 332
4.1.3.3 Risk Appetite Statement 333
Obtain Executive Management and Board Approval 334
Operationalize the RAS, Including Roles and Responsibilities 335
4.1.4 Business Architecture (BA) 337
4.1.4.1 Standardized Operating Model (SOM) 337
Step 1 Finalize Changes 339
Step 2 Standardize the Operating Model 339
Step 3 Improve and Optimize 340
4.1.5 Enterprise Architecture 340
4.1.6 Enterprise Data Architecture & Management
4.1.6.1 GDPR Compliance 344
4.1.6.2 Data for Reporting 344
4.1.7 Enterprise Costing Framework 345
4.1.8 Enterprise Funds Transfer Pricing (FTP) Framework 347
4.1.9 Revision of MR, CR, ALM, and ORM Frameworks 348
4.1.9.1 Revised Market Risk Framework 348
4.1.9.2 Revised Credit Risk Management Framework 350
4.1.9.3 Revised Asset Liability Management Framework 352
Liquidity Stress Testing 354
4.1.9.4 Revised Operational Risk Management Framework 355
Standards 357
4.1.10 Enterprise Stress Testing 358
4.1.11 Capital Adequacy 360
4.1.12 Enterprise Knowledge Management (EKM) 361
Customer Experience 361
Centers of Excellence 362
Chapter 5: Enterprise Architecture 364
5.1 Ontology-Driven Information Systems 366
5.1.1 Core Principles of Enterprise Architecture 367
Reusability, Simplicity, and Flexibility 368
Value Creation 368
5.2 Service-Orientated Architecture (SOA) 369
5.2.1 Overview 369
Elements of SOA 370
5.2.2 Features of SOA 371
Banking Industry Architecture Network (BIAN)2 372
5.2.3 SOA Implementation 373
5.3 Microservices Architecture (MSA) 377
Case Studies 379
5.4 Introduction to Cloud 379
Case Study 380
5.5 Enterprise Event–Driven Architecture 380
5.5.1 Event–Driven Architecture (EDA) Overview 380
Architecture & Technology4
Event-Driven Implementation 384
Events – Operations Management4 385
Event Triggers 385
EDA Governance 386
5.5.2 Complex Event Processing (CEP) 386
Examples of Event-Driven Applications 388
Case Study: Apache Kafka 388
Case Study: Rabobank – Business Event Bus 389
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 392
Fault Tree 393
Event Streaming, TTI-TTC-TTR Application: Case Study 394
TTI-TTC-TTR Explained using the 2007–08 Global Meltdown 394
Time to Cause, 2004–2006 394
The Causes of the 2007-08 Financial Crisis (TTC) 394
Time to Impact, 2006–2008 395
Time to Recover (TTR) 396
5.6 Enterprise Process Automation 397
5.6.1 Process-based Operating Model 397
Banking Process Inventory 397
Top-Down Approach 398
Goal Roll Down to Process Level 398
Enterprise Process Taxonomy & Process-based Risk Metrics
Front-, Middle-, and Back-Office Functions 399
What Constitutes a Good Process? 400
Data & Risk Factors
Risk Management 402
Sub-processes 402
“Called / Invoked Process” 403
5.6.2 BPM Suite Components 403
5.6.2.1 Business Process Modeling 404
5.6.2.2 BPM Engine and Process Orchestration 405
5.6.2.3 Intelligence and Rules Engine 406
5.6.2.4 Enterprise Document/Content Management 407
5.6.2.5 Business Activity Monitoring (BAM) 407
5.6.2.6 Middleware – Enterprise Application Integration (EAI) 407
5.6.3 Process Automation Examples 408
Introduction 408
5.6.3.1 Sales Processes – Four Examples 414
High-Level Retail Sales Process (Asset/Liability) 414
Data Capture of the Lead 414
Sales Process for Personal Loan 415
Sales Process for Home Loan 416
Retail Liability Products 417
Retail Sale of Investment Products 419
5.6.3.2 Retail Banking (More Examples) 420
International Funds Transfer by an Individual 420
Retail – Dormant Account Activation 421
5.6.3.3 Corporate Banking 423
Corporate Long-Term Loan 423
High Level – New Corporate Loan Account Process 423
Customer Identification Program (Reusable Process) 424
Corporate Long-Term Loan Appraisal 425
Corporate Customer On-boarding 426
Corporate Long-Term Loan Approval 428
Corporate Long-Term Loan Disbursement 429
Corporate Banking – Trade Finance 430
Import LC Issuance 430
5.6.3.4 Treasury Processes 432
High-Level View of Hedging 434
Treasury Process Automation Examples 434
FX Forward Contract 434
SWIFT Messages9 436
Interest Rate Swap 436
SWIFT Messages18 439
5.6.3.5 Human Resources 439
Bank Staff 439
Staff & Role Profile Matching
Staff Fraud 441
5.6.3.6 IT Governance 442
5.6.3.7 Risk Management Process 444
High-Level Credit Monitoring Process 444
Fraud and AML-CFT 446
Liquidity and Solvency Risk 449
Enterprise Liquidity Monitoring 449
5.6.3.8 Risk Governance Process 450
High-Level Independent Price Verification (IPV) Process 451
Resolving Unexplained P& L
Market Risk – Fundamental Review of the Trading Book Processes 453
Risk Governance Non-Modellable (NM) Risk Factor (RF) – NMRF 453
Credit Risk-PD Model Governance 456
BPMS for Internal Risk Model Governance 456
GDPR & Processes
5.6.4 Process-based Operational Risk Management 458
Risk Identification & Assessment
Severity / Loss Estimation 459
Control Assessment 459
Corrective Action Testing & Approval
Residual Risk 460
5.6.5 Continuous Process Improvement 462
Process Mining Based on Event & Process Logs, Simulation
Process based Operating Model – Case studies 464
Bank of America – Lean Six Sigma 464
European Bank – SOA, BPMS (IBM case study) 464
TD Banknorth – BPMS 465
5.7 Robotic Process Automation (RPA) 465
Risk Management and Robotic Process Automation 467
5.8 SOA–BPMS Convergence 467
5.9 Enterprise Cost Management 468
Activity-Based Costing 469
Cost of Controls 476
5.10 Gap Resolutions – Enterprise Architecture Category 477
5.10.1 Omni-Channel Platform 477
5.10.2 Financial Inclusion 478
5.10.3 Corporate Banking Improvements 479
Supply-Chain Finance Solution 479
Electronic Bill of Lading 479
Trade Finance Solution – Vendors Collaborate 480
Case Study – Banco Santander 480
Chapter 6: Enterprise Data Management 481
6.1 Data Management Frameworks 481
DAMA-DMBOK1 482
DCAM2 482
6.1.1 DAMA-DMBOK 482
6.1.2 Data Management Capability Assessment Model 483
6.2 Enterprise Data Management 485
6.2.1 Data Taxonomy & Ontology
6.2.1.1 Banking Business Glossary 486
Standardized Data Definitions 486
Glossary and Catalog 487
6.2.1.2 Taxonomy & Ontology
Taxonomy 487
Data Owners 494
European Network and Information Security Agency 494
Ontology 494
Knowledge Management & Ontology
Ontology for a Commercial Bank 495
Metamodel Ontology, Domain Ontology, and Instances 497
Ontology Example - Funds Transfer Pricing (FTP) 497
6.2.1.3 Semantic Web (SW) Technology 498
6.2.2 Business Case for Enterprise Data Management 499
6.2.3 Enterprise Data Management Strategy 500
Focus on Data flow & Lineage, NOT Storage
6.2.3.1 Real-Time Data Processing 500
6.2.3.2 Alignment with Business Strategy 501
Break the Silos 501
6.2.3.3 Align Data Flows with Process Flows 502
Process Automation & Data Lineage
Align Data Flow with Process Flow 503
6.2.3.4 Data as a Service 504
6.2.3.5 Data Streaming (Good Fit for Real-Time Treasury Management) 504
6.2.3.6 Data Ownership 504
6.2.3.7 Data Sharing, Interoperability, and Reusability 505
6.2.3.8 Centralized vs Decentralized 505
6.2.3.9 Defensive and Offensive Data 505
6.2.3.10 Data for Analytics and Knowledge Management 506
Data Science 506
6.2.3.11 Data Protection and Privacy 506
6.2.4 Enterprise Data Model & Architecture
6.2.4.1 Enterprise-wide Data Discovery 507
6.2.4.2 Target Enterprise Data Model 509
Data Models – Canonical & Logical
Conceptual, Logical, Physical Models 509
6.2.4.2.1 Master, Reference, Metadata, Transaction 509
Types of Data 509
Master Data Management 510
Reference Data Management (RDM) 512
Metadata 514
Transaction Data4 518
Lines of Business and Human Capital Data Models 518
6.2.4.2.2 Enterprise IT Governance Data 522
6.2.4.3 Data as a Service 523
6.2.4.4 Data Streaming 526
6.2.4.5 Lambda Architecture 527
6.2.4.6 Kappa Architecture 528
6.2.4.7 Protocols for Financial Messaging 529
The Interactive Financial eXchange (IFX) 529
The Financial Information Exchange protocol (FIX) 529
An Example of Payment Infrastructure Security 529
6.2.5 Enterprise Data Management Technology 529
6.2.5.1 Enterprise Data Technology 530
Data Virtualization (DV) 530
Data Integration 530
Data Abstraction9 531
Federation vs Integration 531
Big Data 535
6.2.5.2 Database Management System (DBMS) 536
NoSQL 536
Graph Databases 537
Building a Graph Database Model 538
DBMS Comparison 540
RDBMS, Hierarchical & Graph Database
Data Warehouse 541
Data Lakes 542
Knowledge Graph 543
Data Catalog 544
Case Study for Data Catalog 544
6.2.5.3 In-Memory Technology 544
In-memory Databases (IMDB) 545
Case Study – International Software Solution Vendor 546
6.2.6 Data Management Program 547
Data Management Phases 548
Data Maintenance 548
Data Synthesis 548
Data Usage 548
Data Publication 548
Data Archival 549
Data Purging 549
6.2.7 Data Quality and Lineage 549
6.2.7.1 Data Standard ? Data Quality 550
Examples of Some Standards 550
Financial Products Markup Language, ISO20022 550
Payment Card Industry, Data Security Standards Council (PCI_DSS) 551
Payment Security Directive_2 552
6.2.7.2 Data Quality Framework 552
Define a Data Quality Measurement Framework 552
Legal and Institutional Environment 553
Financing Accounting & Risk Computation – Standards &
Accuracy and Reliability of Data 556
Approach to Assessing Data Quality 557
Three Data Quality Capabilities 558
Method for Data Audit 558
Integrity 559
6.2.7.3 Data Lineage 559
Data Lineage Analysis 559
Data Lineage Dimensions 560
Data-Item Relationships 560
Event-Driven Architecture, BPMS, and Event Logs Establish Data Lineage 561
Master Data & Metadata Quality Management
Case Study 562
Process Automation, Data Lineage, and Traceability 563
The Third Checkpoint – Enterprise Risk-Adjusted Return Management 566
Automated Data Lineage 567
6.2.8 Data Control Environment 567
6.2.8.1 Enterprise Data-Centric Security Model 568
Derivation of Business Controls 569
Derivation of Technical Controls 571
6.2.8.2 Data Classification 573
Accessibility to Data 575
6.2.8.3 The GDPR Perspective 575
Johari Window for Data Privacy 577
Data Protection and Privacy Needs 577
Open Banking 578
Data-Sharing Models 579
NIST & IAPP
6.2.8.4 Data Transmission 584
Security of Database Objects 585
6.2.8.5 International Payments 587
SWIFT – Mandatory Security Control Framework for Members 587
6.2.8.6 Data Lineage and Algorithms 588
6.2.9 Data Governance 589
Data Governance Council (DGC) 590
Enterprise Data Governance (EDG) Principles 590
Enterprise Data Policy 591
6.2.9.1 Master Data Governance (MDG) 593
Chart of Account Classification 594
Inputs for Charts of Accounts 21 595
Enterprise Cost Allocation 595
Funds Transfer Pricing 595
Market, Credit, and Operational Risks 596
Enterprise Liquidity Management 596
6.2.9.2 Metadata Governance20 596
6.2.9.3 Reference Data Governance20 596
6.3 Reference ERRM Architecture 597
6.3.1 Reference Enterprise Architecture for the ERR Model 598
Event-Driven, Data-Centric, Process-Automated Enterprise Risk–Return Management 598
Chapter 7: Enterprise Risk Data Management (A Subset of Enterprise Data Management) 602
7.1 Enterprise Risk Ontology 605
7.1.1 Risk Data 605
Risk Data Characteristics 605
7.1.1.1 Scalar Data 605
7.1.1.2 Numerical and Categorical Data 606
7.1.1.3 Levels or Scales of Measurement 606
7.1.1.4 Dimensionality 607
7.1.1.5 Synchronous and Non-synchronous Data 607
7.1.1.6 Curves and Data Requirements 608
Yield Curve Data 608
Basis Curve Data 608
LIBOR Forward Curve Data 609
Secured Overnight Financing Rate-Forward (SOFR) Curve Data 609
Swap Curve 609
7.1.2 Business Glossary 610
7.1.3 ERRM Taxonomy 611
ERRM Taxonomy, RAF & RAS
Enterprise Risk and Performance Taxonomy 612
7.1.3.1 Treasury Taxonomy 613
ISDA Common Domain Model (CDM) Taxonomy1 614
7.1.3.2 Credit Risk Management Taxonomy 615
7.1.3.3 Liquidity Risk Management, ALM Taxonomy 615
Interest Rate Risk in the Banking Book (IRRBB)2 616
7.1.3.4 Operational Risk Management Taxonomy 617
TARA Implementation Steps 618
OCTAVE 619
7.1.3.5 Stress-Testing Taxonomy 620
7.1.4 Risk Data Dictionary 621
Banking Data Ontology – Efforts by Stakeholders 623
Time-Series Data Management 625
Business Glossary & Data Dictionary
7.1.5 Enterprise Risk-Adjusted Return Ontology 627
Financial Industry Business Ontology (FIBO) 628
7.1.5.1 Risk Data Classification 628
Classification Impact of Risk Data on Financial Data 629
7.1.5.2 The Ullman Triangle 630
7.1.5.3 ERRM Ontology 631
7.1.5.3.1 Market Risk – Examples 631
Centralized Collateral Management Ontology 634
Collateral Valuation 635
Market Risk, Standardized Approach, Credit Value Adjustment Ontology 637
Credit Value Adjustment (CVA)6 637
Hedging Ontology 638
Black Scholes Pricing (BSP) Simulation for Delta Hedging 640
Dynamic Hedging8 642
Ontology for Treasury – FIBO Case Study 643
7.1.5.3.2 Credit Risk 643
7.1.5.3.3 ALM Ontology 647
High-Quality Liquid Assets (HQLAs)9 649
Calculation9 651
Centralized General Ledger 651
7.1.5.3.4 Process-based Operational Risk Ontology 653
7.1.5.3.5 Enterprise IT Governance 656
7.1.5.3.6 Human Capital 657
7.2 Ontology-based ERRM System 658
7.3 Enterprise Risk–Return Data Strategy 660
7.3.1 International Effort – Data Standardization 661
7.3.1.1 ISDA’s Common Domain Model (CDM)10 661
7.3.1.2 CPMI-IOSCO 661
Timestamping 663
7.3.1.3 LEI, ISIN 663
Legal Entity Identifier, ISO 17442 663
International Securities Identification Numbering system 664
7.3.1.4 FIGI – Financial Instrument Global Identifier 664
7.3.1.5 Data Governance Issues 664
7.3.2 Enterprise Risk-adjusted Performance Metrics 665
RAS and Early Warning Signal, KRIs, KPIs 665
7.3.3 Event-Driven Offensive and Defensive Data Management 667
Risk Data Strategy 667
7.4 Enterprise Risk Data Discovery 670
7.4.1 Risk Management Data Requirements 671
Two Phases or Passes 671
7.4.1.1 Product Risk 671
Risks Inherent in Products 671
Financial Instrument Characteristics 672
Product Risk Classification (PRC) 676
Simultaneous Checking of All Risk Types 676
7.4.1.2 Customer Profile 677
7.4.1.3 Backward Pass 677
Banking Supervisor’s Statistical Data Warehouse 678
Risk Profile and Risk-Weighted Assets 680
7.4.1.4 Forward Pass – Product & Process Driven
Market Risk 681
Data-flow Diagram 682
Entity-Relationship Diagram 685
Case Study 685
Barclays 2018 Project to Evaluate ISDA’s Common Domain Model (CDM) 685
Conclusion 686
Data Discovery – FRTB, IRRBB, CSRBB 686
Data Classification11 688
Sensitivity-based Standardized Approach31 689
Default Risk Charge (DRC)11 691
Residual Risk Add-on (RRAO)11 691
Credit Value Adjustment (CVA)11 691
Internal Model 13 692
Classification of Risk Factors as Modellable 11 692
Non-modellable11 693
Ontology Aspect in Risk Factor Eligibility Test (RFET)12 Determination 694
P& L Attribution Test (PLAT) 11
CSRBB11 697
IRRBB – Data Flow13 698
IRRBB Governance 699
Simplified Standardized Approach14 700
Enterprise Credit Risk 701
Counterparty Credit Risk (CCR) 701
Lending 701
Credit Risk Monitoring – Data Discovery 703
ALM, Funding & Capital Adequacy
Net Stable Funding Ratio (NSFR)16 705
Common Equity Tiers17 707
Operational Risk Management (ORM)18 708
Internal Loss Data (ILD) 709
Operational Risk Data Flow and Model 710
External Loss Data (ELD) 711
Business Environment and Internal Control Factors (BEICF) 711
Scenario Analysis 712
7.4.2 Master, Meta, Reference, Historic, Time-Series, Transaction Data 713
7.4.2.1 The Approach 714
Data Elements 715
7.4.2.2 Risk Master Data Management (Risk MDM) 715
7.4.2.3 RDM as a Service 716
Reference Data Management (RDM) 716
7.4.2.4 Risk Metadata Management 716
Risk Dataset Template 717
The Risk Catalog – Master, Meta, and Reference Data 717
7.4.2.5 Historical Data 717
7.4.2.6 Time-Series Data19 718
Time-Series Data for MR, CR, and ALM 719
7.4.2.7 Transaction Data, Risk Calculations 719
7.4.2.8 Synthetic Data, Data Quality, and Data Lineage 721
7.4.3 Enterprise Data Standardization 722
Standardized Operating Model and Data Standardization 722
7.4.4 Enterprise Risk Data Catalog 723
7.5 Event-Driven, Data-Centric ERRM 724
Event-Driven Architecture (EDA), Process Automation 724
Event-Driven, Threat–Asset–Vulnerability (TAV) Approach 726
7.5.1 Event Driven 728
Event Triggers Business Activities 728
7.5.1.1 Treasury 729
Event Automating ISDA Documentation20 729
7.5.1.2 Credit Risk Data Flow and Model 730
7.5.1.3 Event-Driven, Data-Centric ALM 731
7.5.2 Risk Register & Events
Fault Tree Analysis (FTA) 733
Cause–Risk–Consequence / Cause–Event–Consequence, Ontology Model 733
Event Tree Analysis (ETA) 734
7.5.3 State Transitions, Actions & Events
State21 738
Transition (can be positive or negative)21 739
Action21 739
Bank ATM21 739
A Bank’s Internal Credit Rating System 740
Risk Transmission 741
7.5.3.1 Markov Chain 741
7.5.4 Data State Transition Diagrams (DSTD) 742
Advantages of Using State Diagrams 743
7.5.5 Process Mining & State Transition
Evidence-based BPM Minimizes Risks, Maximizes Returns 743
Process Mining and Enterprise Liquidity Management 744
7.6 Risk Data Management Technology 745
7.6.1 Time-Series Database22 745
7.6.2 In-memory Management and Graph Database Applications 746
Event Trees and GraphDB23 747
GraphDB for Lattice Structure 748
Anti–Money Laundering, Countering the Financing of Terrorism 748
Know Your Customer, Due Diligence, and Enhanced Due Diligence 749
Complex Corporate Structures, Layered Identities 750
7.6.3 C++, Python, R Programming 750
7.7 Multi-dimensional Enterprise Risk Data Model 751
7.7.1 Adaptation of Data Point Model for Enterprise Risk Data Model 751
Multi-dimensional Enterprise Risk–Return Data Model 751
7.8 Approach to Assessing EDM Maturity 753
Major Components of the Data Governance Maturity Model 753
Chapter 8: Data Science and Enterprise Risk–Return Management 756
8.1 Math & Stats in Risk Data Calculations
Introduction to Different Disciplines of Mathematics and Statistics 759
Linear algebra 759
Example: Cholesky Decomposition 760
Trigonometry 760
Calculus 760
8.1.1 Elementary Statistics 761
Population and Sample 762
Parameter and Statistic 762
Variable, Observation and Random Variable 762
8.1.1.1 Covariance 763
8.1.1.2 Correlation 763
8.1.1.3 Correlation Coefficients 764
Pearson’s Correlation 765
Spearman’s Rank Correlation (?) 766
Kendall’s Rank Correlation (?) 766
Partial Correlation 767
8.1.1.4 Bootstrapping Data 767
8.1.2 Distributions 768
8.1.2.1 Continuous Probability Distribution 768
Normal Distribution 768
Standard Normal Distribution 770
Student’s T Distribution 771
Chi-Square Distribution 773
Exponential Distribution 775
Properties of Exponential Distribution 777
Pareto Distribution 777
Log-Normal Distribution 779
Weibull Distribution 780
8.1.2.2 Fitting Loss Distributions 782
Measure of the Goodness of Fit (AIC, BIC) 782
Analyzing the Fit of Loss Distribution 783
Exponential Distribution 785
Weibull Distribution 786
Pareto Distribution 786
Gamma Distribution 787
Log-normal Distribution 787
Conclusion for This Example on Fitting Loss Distribution 788
8.1.2.3 Discrete Probability Distributions 788
Binomial Distribution 789
Poisson Distribution 790
Geometric Distribution 791
Negative Binomial Distribution 793
8.1.2.4 Selection of Data Distribution (e.g., for Operational Risk) 795
Loss Frequency and Severity Distribution5 795
Binomial Distribution 797
Poisson Distribution 798
Loss Severity Distribution 800
Log-Normal Distribution 800
Operational Loss Distribution 801
Combining Loss Frequency with Loss Severity5 801
8.1.3 Parametric Models and  Non-parametric Alternatives 803
Parametric and Non-parametric Statistical Tests 803
8.1.3.1 Z Test 804
8.1.3.2 t-Test 804
8.1.3.3 F-Test 805
8.1.3.4 ANOVA (Analysis of Variance) 805
8.1.3.5 Non-parametric Tests Used for Measuring Risks 806
Comparison of Statistical Tests 808
8.1.4 Discriminant Analysis 809
8.1.5 Deterministic, Probabilistic, Stochastic Models 809
Probabilistic or Stochastic Models 810
Stochastic Process and Model 810
8.1.6 Receiver Operating Characteristic (ROC) Curve 811
8.1.7 Line of Equality, Concentration Measures 812
Lorenz Curve, Gini Coefficient & Herfindahl–Hirschman Index
Lorenz Curve 812
Gini Coefficient8 813
Credit & Market Risk Management
Herfindahl–Hirschman Index (HHI) 814
8.1.8 Regression Analysis 814
8.1.8.1 Simple Linear Regression (SLR) 815
8.1.8.2 Multiple Linear Regression (MLR) 817
R2, Adjusted R2 818
Durbin Watson 819
8.1.8.3 Non-linear Regression 820
Binary Logistic Regression 820
Poisson Regression Analysis 821
Probit Regression 821
Smoothing Spline 822
Confidence Interval (CI) 822
8.1.9 Risk Management – Statistical Usage 823
8.1.10 Data Bias 825
Statistical Bias 825
8.2 Theory and Concepts 826
8.2.1 Uncertainty in Risk-Return 827
8.2.1.1 Mean Reversion, Mean Reversion Indicator Set 827
8.2.2 Portfolio Theory 828
Portfolio Theories 828
8.2.2.1 Random Walk Theory2 828
8.2.2.2 Martingale 829
8.2.2.3 No Arbitrage Hypothesis 829
8.2.2.4 CAPM and APT 829
Capital Asset Pricing Model (CAPM) with Arbitrage Pricing Theory (APT) 829
Arbitrage Pricing Theory (APT) 830
8.2.2.5 Dynamic Global Immunization Theorem (Uses Portfolio Duration) 830
8.2.3 Risk-Neutral Pricing 830
8.2.4 Probability Theory and Information Theory 831
8.2.4.1 Frequentist vs. Bayesian Probability 832
8.2.4.2 Bayesian Statistics 832
Bayesian Inference 832
Bayes’ Theorem 833
8.2.5 Law of Large Numbers (LLN) 833
8.2.6 The Central Limit Theorem (CLT) 833
8.2.7 The Fourier Transform 833
8.2.8 Euler Theorem and Allocation 834
8.2.9 Markov Chain 834
8.2.10 Factor Models 835
Linear Factor Model 835
Dynamic Factor Model 836
8.2.11 Eigen Decomposition of the Covariance Matrix 836
8.2.12 Stochastic Differential Equations (SDE) 837
Stochastic Differential Equation (SDE) Taxonomy 838
8.2.13 Brownian Bridges 839
Stochastic Simulation of Interest Rate Paths 839
8.2.14 Structural and Reduced Form Models 839
8.2.15 Enterprise Cause–Event–Consequence Discovery 840
Causal Analytics 840
Event Log for Causal Analysis 841
8.2.16 Causal Loops and TTC, TTI, and TTR 844
8.2.16.1 Causal Loops 844
8.2.16.2 Time to Cause, Time to Impact, Time to Recover 845
Market Efficiency, Information, TTC & TTI
8.2.17 Tail Behavior 847
8.2.17.1 Extreme Value Theory (EVT) 847
Block Maxima 847
Generalized Extreme Value (GEV) 847
Peak Over Threshold (POT) 848
8.2.17.2 Expected Shortfall 848
Theory, Concepts & Occam’s Razor Principle
8.3 Risk Management Models 849
8.3.1a Time-Series Models 849
Model Fitting 851
8.3.1b Correlation Model Taxonomy 851
8.3.2 Market Risk (MR) Models 852
8.3.2.1 Multi-factor Models 852
Risk-Neutral Density Models 853
8.3.2.2 Option-Adjusted Spread (OAS) 853
8.3.2.3 Hull–White Tree – Term Structure 854
8.3.2.4 Yield Curve Construction Models 854
8.3.2.5 EMV Model for Portfolio Behavior 855
Exogenous, Maturity, Vintage (EMV) 855
8.3.2.6 Asset Allocation Models 855
Markowitz and the Black–Litterman Model 855
8.3.2.7 Interest Rate Models 855
LIBOR Market Model (LMM) 856
Vasicek & CIR
Nelson and Siegel 857
Nelson–Siegel–Svensson 858
Heath, Jarrow & Morton
Interest Rate Model – Evaluation Criteria 858
8.3.2.8 Statistical Decomposition, Eigen Portfolios 858
8.3.3 Credit Risk 859
8.3.3.1 Loan Portfolio Optimization 859
8.3.3.2 Survival Models (Credit Risk – Recovery) 859
8.3.3.3 Probability of Default Model 860
Default Analysis 860
8.3.4 Asset Liability Management (ALM) 862
8.3.4.1 Merton Model 862
8.3.4.2 ALM Strategies 862
Multi-period Stochastic Models 862
Dynamic Financial Analysis (DFA) Model 862
Non-maturity Deposits (NMD),32 862
Behavioral Model for Retail Depositors 863
8.3.5 Operational Risk 863
8.3.5.1 PetriNets 863
8.4 ERR Model Governance 864
8.4.1 Statistical Information System 864
8.4.2 ERR Modeling Ecosystem 865
8.4.2.1 The Taxonomy of Risk Models 865
8.4.2.2 Risk Modeling Ecosystem 866
8.4.3 Risk–Return Model Management 867
8.4.3.1 Policies and Procedures 868
8.4.3.2 Model Design and Code (not applicable for vendor-supplied models) 869
Market Risk 871
Synthetic Data for Risk Management 871
Model to Generate Synthetic Data 872
8.4.3.3 Model Testing 874
8.4.3.4 Model Testing Documentation 874
8.4.3.5 Model Approval and Deployment 875
8.4.4 Interconnected Models 876
8.4.5 ERR Model Governance 877
Sandbox Environment for Risk–Return Model Governance 877
8.4.5.1 First Line of Defense 877
8.4.5.2 Second Line of Defense 877
8.4.5.3 Model Audit (Third Line of Defense) 878
Chapter 9: Advanced Analytics and Knowledge Management 882
9.1 Advanced Analytics 882
9.1.1 Descriptive, Prescriptive, Predictive, Discovery 883
Descriptive Analysis 883
Predictive Analysis 884
Analytics Moves from Rule Based to Intelligence 885
Event-based Process Orchestration 885
Prescriptive Analytics 886
Discovery Analytics 886
9.1.2 Algorithm 887
9.1.3 Machine Learning 888
9.1.3.1 The Three Model Categories 888
Supervised & Unsupervised Learning
The Confusion Matrix 889
Reinforcement Learning (RL) 890
Machine Learning Model Creation & Usage
9.1.3.2 Machine Learning – Models, Methods, Techniques 892
Dimension Reduction Models 892
Principal Components Analysis (PCA) 893
Singular Value Decomposition (SVD) 894
Independent Component Analysis (ICA) 894
Neighborhood Component Analysis (NCA) 894
Other Models, Methods, Techniques 894
Classification and Regression Trees (CART) 894
Ross Quinlan Decision Trees 895
Gradient Boosting, Bayes Classifier 896
Ensemble Methods in Machine Learning 896
Understanding the Ensemble Method by Referring to Decision Trees 896
Model-Based Reinforcement Learning (RL) 898
State, Action, and Reward 898
Markov Decision Process (MDP) 899
Boosted Decision Tree Model 899
K-Means, K-Medoid Clustering 899
Dynamic Programming (DP) 899
Genetic Programming 900
Bayesian Optimization 900
9.1.3.3 Pregel – Processing Large-Scale Graphs 901
9.1.4 Neural Networks (NN) 901
9.1.4.1 Self-Normalizing Neural Networks (SNN) 902
9.1.4.2 Shallow Neural Networks 902
9.1.4.3 Deep Neural Networks 902
9.1.4.4 Backpropagation 902
9.1.4.5 Perceptron 903
9.1.5 Overfitting or Underfitting the Data 903
9.1.6 Deep Learning 904
9.1.7 Reference Advanced Analytics Functional Architecture 905
9.2 Knowledge Management (KM) 906
9.2.1 Ontology-driven Knowledge Management (KM) 906
9.2.2 KM Methodology 907
Identify, Acquire & Create6
Harness – Store & Share6
Harvest – Apply (KM Cubes) & Use6
9.2.2.1 KM Work Breakdown Structure 911
9.2.2.2 “How to KM” in an ERRM Context 912
Know What 912
Know Why 913
Know How 913
Facet Analysis 914
Know Where 914
9.2.2.3 Continuous Improvement 915
9.2.3 Knowledge Graphs (KG) 915
9.2.3.1 Knowledge Graphs and Machine Learning 918
Describing New Relations using Machine Learning 918
Connectedness 919
Gap Resolutions 921
9.3 KM and AA Applications 921
9.3.1 Sales & Marketing
9.3.2 Risk Profiles 922
9.3.3 Behavioral Analytics – Customer & Staff
Deep Learning with Keras Library to Predict Customer Churn 922
9.3.4 360° View of Human Capital (Employee) 923
9.3.4.1 Employee Capability Measurement 925
9.3.5 360° View of Customer 927
9.3.5.1 State Transition Model and Credit Risk 928
Credit Migration 928
Credit Card Accounts 929
9.3.5.2 Customer Segmentation 929
9.3.5.3 Machine Learning and Chatbots 930
9.3.5.4 Gamification 930
9.3.5.5 Customer Experience 931
9.3.6 Transaction Analysis 931
9.3.6.1 Natural Language Processing (NLP) 931
9.3.6.2 Sentiment Analysis 932
9.3.6.3 Learning Customer Interaction 932
9.3.7 Enterprise Fraud Prevention 933
Neural Network System for Fraud Prevention 935
Bank Card Fraud Detection using ANN 936
Credit Card Fraud Analytics & Knowledge Graph
9.3.8 Anti–Money Laundering & Countering the Financing of Terrorism (AML-CFT)
Risk Mitigation Priority Weights 938
9.3.9 Treasury Trading & Deep Learning
Regularization Algorithms 939
LASSO & Ridge Regression Technique for Automatic Trading Advice
Regularization Used for Proxy Hedging 940
Scenario Trees and Interest Rate Path Approaches 940
Scenario Tree 940
Treasury – Big Data, Stream Computing & Machine Learning
9.3.10 Enterprise Liquidity Management (ELM) 941
Maximum Entropy Principle 942
Using Behavioral Analytics for IRRBB, Liquidity Management 942
Non-maturing Deposits 942
9.3.11 Wealth Management 943
9.3.12 Credit Risk Management 943
Ensemble Learning Methods and Credit Risk Management 944
K-Means Clustering 944
9.3.13 Banking Operations 945
9.3.13.1 Branch Performance using K-means Clustering 945
9.3.13.2 IT Risk – Knowledge Management 945
TARA, CRAMM, OCTAVE – KM Methods 946
9.3.14 Enterprise Content Management (ECM) 949
9.3.15 Banking Case Studies 949
Case Studies 950
I. JPMC 950
II. BBVA 951
III. Dutch Lender ING 951
IV. Commonwealth Bank of Australia 951
V. Triodos Bank 951
VI. Synthetic Data 952
VII. Algo-driven Non-deliverable Forward Trade Execution 952
VIII. Banking Supervisor/Central Bank 952
Summary 952
9.4 Analytics Maturity Evaluation 953
Data Analytics Maturity Phases 954
Enterprise Knowledge Management 955
Chapter 10: ERRM Capabilities & Improvements
10.1 Enterprise Liquidity Management (ELM) 958
10.1.1 Liquidity Assessment Principles 959
10.1.2 Basel III – Liquidity Risk Framework, Main2 963
10.1.3 Implementing the New ELM 965
10.1.3.1 Standardized Operating Model (SOM) 966
10.1.3.2 Chart of Accounts 966
10.1.3.3 Real-Time Payments 966
RTGS 966
SWIFT 967
Nostro Management3 967
Real-Time Nostro Management 968
Liquidity Implementation Task Force (LITF)4 968
SWIFT, LITF & Enterprise Liquidity Management
SWIFT Instant Payment and Domestic Direct Payment5 970
Back-Dated Entries and Forward Value–Dated Entries 972
gpi Tracker7 973
10.1.3.4 Treasury8 Centralized Real-Time Collateral Management 975
Real-Time Collateral Management 975
Dynamic Hedging 977
10.1.3.5 Enterprise Liquidity Hub (ELH) 977
Enterprise Liquidity Hub Design 977
Unlocking the “Liquidity Trapped in Silos” Model 978
Virtual ELH 979
Physical ELH 979
Liquidity Risk Event 981
Normal & Stressed Cash Flows
Cashflow Predictions using Machine Learning-Ensemble Prediction Model 983
Liquidity Risk Simulation 985
Earnings at Risk Model10 986
Liquidity Management – TTC, TTI, and TTR 988
Time to Cause 988
Time to Impact – Multiple Shocks 988
Time to Recover 989
System Dynamics (SD) for ELM11 990
The “Offensive Use” of the Enterprise Liquidity Hub (Events and Data) 991
Global Transaction Banking (GTB) 991
10.1.4 Liquidity Stress-Testing Framework 992
Four Type of Shocks 993
Enterprise Liquidity Stress Testing 995
Liquidity Shortfall under Stress 995
Stress Test Output – Corrective Action 996
Liquidity Risk Tail Events 997
10.1.5 Developing a Contingency Funding Plan (CFP) 997
Stress Event Types and CFP 998
10.1.6 Monitoring Intra-day Liquidity Risk14 999
Risk Appetite Monitoring 1000
10.1.6.1 Internal Enterprise Asset Liquidity Index 1001
10.1.6.2 Funding Matrix 1001
Intra-Day Liquidity Controls 1003
10.1.7 Introduction to Cash Flow @ Risk 1005
Cash Flow at Risk (CFaR)15 1005
10.2 Dynamic ALM16 1006
Dynamic Sources & Dynamic Uses
Case Study – Liquidity Risk Can Make Banks Insolvent 1009
10.3 Liquidity Transfer Pricing (LTP) 1009
10.3.1 LTP as Part of FTP 1011
Liquidity Buffer, Cost of Carry, and LTP 1011
Liquidity Cushion – Cost of Carry Allocated using LTP Metrics 1012
FTP and Enterprise Data Management 1014
10.4 Improvements to Balance Sheet Optimization21 1017
Common Equity Tier 121 1018
Additional Tier 1 Capital Preference Shares21 1018
Tier 2 Capital21 1018
10.4.1 Balance Sheet Projections 1021
Risk Weighted Assets (RWA) Optimization 1023
Management, ALCO, Risk Committees – Risk Appetite Framework & Statement
10.4.2 An Illustrative Optimization Approach 1024
Bank Ratings 1026
Machine Learning, Deep Learning 1027
10.5 Improved Risk Measures 1027
10.5.1 Process & Operating Model Maturity
Residual Process Risk 1027
Process Risk Score 1028
Process Maturity 1028
Process Risk Score and Process Maturity 1030
Process Maturity 1031
External Loss Calibration using Process Maturity Score 1032
Operating Model Maturity 1033
10.5.2 Liquidity-adjusted Market Risk 1036
Market Risk & Liquidity Risk
10.5.3 Liquidity-adjusted Credit Risk 1040
Using Credit Default Swap (CDS) Bid–Ask 1040
Vector Auto-Regressive Model 1041
Cross-Correlation between Credit Risk and Liquidity Risk 1042
10.5.4 Risk-Adjusted NIM23 1042
The Holistic Balance Sheet View 1043
10.5.5 Tail Behavior 1044
Kurtosis 1045
Rate of Survival Function 1045
Regularly Varying Function 1046
Sum of Independent Random Variables 1047
10.5.6 Expected Shortfall / Conditional VaR 1047
Expected Shortfall under Regular Variation 1048
Maximum Domain of Attraction 1049
Estimation of ES 1050
Normal Distribution 1050
T Distribution 1050
Non-parametric Methods for Estimating Expected Shortfall 1051
Forecasts for Expected Shortfall 1052
Back-testing of Expected Shortfall 1052
10.6 Copulas for Measuring Enterprise Risk 1054
10.7 Bank-wide Stress Testing 1060
Bank-wide Stress-Testing Framework24 1061
Conclusion 1065
C1. Enterprise Approach to Maximizing Risk-Adjusted Returns 1065
Risk Governance 1065
Single ERM Measure 1065
Expected Shortfall and Backtesting 1066
Risk-Adjusted NIM, Liquidity-Adjusted VaR 1066
Enterprise Stress Testing 1067
Risk-Weighted Asset (RWA) Optimization 1067
C2. Enterprise Architecture 1069
Ontology-based Information Systems 1069
Services and Micro-Services Architecture Orientation 1069
Event-Driven Architecture 1069
C3. Technology 1070
Robotics Process Automation (RPA) 1070
C4. Enterprise Data Management Technology 1070
Data Virtualization (DV) and Data as a  Service (DaaS) 1070
In-Memory Computing 1070
Graph Database and Knowledge Graphs 1071
Knowledge Management 1071
Machine Learning 1071
AI and ML Usage Challenges 1072
Sandbox – Modeling & Regulatory Needs
PSD2 and GDPR 1074
Statutory Audit Qualification 1075
Polyglot Persistence 1075
Data Ops 1075
C5. Climate Change & Banking
The Equator Principles 1075
C6. Data Is the Lifeblood of ERR Management 1076
Appendix A: Abbreviations 1077
Appendix B: List of Processes 1083
Bibliography 1085
Chapter 1 1085
Chapter 2 1086
Chapter 3 1089
Chapter 4 1091
Chapter 5 1092
Chapter 6 1093
Chapter 7 1095
Chapter 8 1097
Chapter 9 1102
Chapter 10 1104
Index 1107

Produkt-Details

• Erscheint lt. Verlag: 3.1.2022
• Zusatzinfo: XXVIII, 1090 p. 639 illus.
• Sprache: englisch
• Themenwelt: Informatik ► Netzwerke ► Sicherheit / Firewall
• Themenwelt: Naturwissenschaften
• Themenwelt: Recht / Steuern ► Wirtschaftsrecht
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Allgemeines / Lexika
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management
• Schlagworte: Bank Enterprise Architecture • Banking • commercial banks • Data Management in banks • event driven architecture • knowledge management • machine learning • Ontology • Process Automation • Risk data science • Risk Management • Risk Measurement and Management
• ISBN-10: 1-4842-7440-7 / 1484274407
• ISBN-13: 978-1-4842-7440-8 / 9781484274408