Risk Analytics: From Concept to Deployment (World Scientific Financial Data Analytics)

Contents
Foreword
About the Author
Chapter 1 Introduction
Chapter 2 Risk Typology and Data Implications
	2.1. Data Needed
	2.2. Data Stewardship
	2.3. Deployment for Use
Chapter 3 Risk Analytics Landscape
	3.1. Software and Solutions
		3.1.1. Request for Proposal (RFP)
		3.1.2. Terms of Reference (TOR)
		3.1.3. Proof of Concept (POC)
		3.1.4. System Integration Test (SIT)
		3.1.5. User Acceptance Test (UAT)
	3.2. Data Table and Data Type
	3.3. Modeling
		3.3.1. Business understanding
		3.3.2. Data understanding
		3.3.3. Data preparation
		3.3.4. Modeling
		3.3.5. Evaluation
		3.3.6. Deployment
	3.4. Data Flow
		3.4.1. Data quality and quantity
		3.4.2. External and internal data integration
		3.4.3. Security and access
		3.4.4. Analysis and decision-making
	3.5. Deployment
	3.6. Governance
	3.7. Need for Integration
Chapter 4 Embedded R
	4.1. ORACLE_HOME and R_HOME
	4.2. Pluggable Database
	4.3. RQUSER
	4.4. R Packages and RStudio
	4.5. Oracle R Enterprise (ORE)
Chapter 5 Data Audit
	5.1. Missing
	5.2. Invalid
	5.3. Unreliable
	5.4. Outdated
	5.5. Inconsistent
	5.6. Data Audit Report
	5.7. Treatments for Problematic Data Values
Chapter 6 Data Warehousing
	6.1. Legacy System Data
		6.1.1. Overwrite mode
		6.1.2. Record removal
		6.1.3. Legal priority
		6.1.4. Data type
	6.2. Enterprise Data Warehouse vs Specialized Data Mart
		6.2.1. Data stewardship
		6.2.2. Access and security
		6.2.3. Update frequency
		6.2.4. Designed to fail
	6.3. Extraction, Transfer, Load (ETL)
		6.3.1. Data cleansing
		6.3.2. Mappings
		6.3.3. Rejected records
		6.3.4. Corrections
Chapter 7 Analytical Data Sphere
	7.1. Archive, Not Overwrite
	7.2. Dropdown List, Not Free Text
	7.3. Meaningful Categories
	7.4. Optimal Number of Categories
	7.5. Expandable Data Tables
	7.6. Dated
	7.7. Useful and New Primary Keys
	7.8. Updatable
	7.9. Accessible
Chapter 8 Risks in Financial Institutions
	8.1. Profiling What Is Ahead
	8.2. External Warning Indicators
	8.3. Operational Concerns
	8.4. Portfolio Composition
Chapter 9 Common Risk Models and Analytics
	9.1. Expected and Unexpected Losses
	9.2. Value at Risk
	9.3. Securities Portfolio Optimization
	9.4. Correlation
	9.5. Concentration Index
	9.6. Operational Loss Distribution
	9.7. Stress Testing
	9.8. Weight of Evidence
Chapter 10 Internal Rating System
	10.1. Developing an ORR
	10.2. Data Audit
	10.3. Predictors and Target
	10.4. Weight of Evidence (WoE)
	10.5. Training a Model
	10.6. Risk Grades/Ratings
	10.7. Backtesting
Chapter 11 Deployment
	11.1. Default and Reageing
	11.2. Enterprise Data Warehouse or Data Mart
Chapter 12 Through The Cycle (TTC) Updating
Chapter 13 Desktop Analytics
	13.1. Basic Excel R Toolkit (BERT)
	13.2. Probabilities and PD
		13.2.1. Cumulative and marginal probabilities
		13.2.2. Joint, conditional and unconditional probabilities
		13.2.3. Binomial lattice
	13.3. Loss Given Default (LGD)
	13.4. Credit Valuation Adjustment (CVA), Debit Valuation Adjustment (DVA) and xVA
		13.4.1. Spot, forward and par rates
		13.4.2. Interest rate binomial lattice
		13.4.3. Counterparty default
	13.5. R with Excel
Chapter 14 Resources
	14.1. RStudio
	14.2. Packages
	14.3. Free Data
Annex A: Meeting of Minds Questionnaire
	Business Problem and Solution
	Departments and Individual Users
	Budget
	Working Mode
	Data Collection and Storage
	Analytics Tool
	OLAP and Front-End Tools
	Other Information
Annex B
Index