دانلود کتاب Credit Intelligence & Modelling: Many Paths through the Forest of Credit Rating and Scoring

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Cover Credit Intelligence & Modelling: Many Paths through the Forest of Credit Rating and Scoring Copyright Dedication Foreword Shifting Seas The Toolkit Forest Paths Acknowledgements Language & Syntax Presentation—Warnings Kindle e-Book—Warnings Table of Contents Module A: Introduction 1: Credit Intelligence 1.1 Debt versus Credit 1.2 Intelligence! 1.2.1 Individual Intelligence 1.2.2 Collective Intelligence 1.2.3 Intelligence Agencies 1.2.4 Intelligence Cycle 1.3 The Risk Lexicon 1.3.1 What is . . .? 1.3.1.1 Credit Intelligence 1.3.1.2 Credit risk 1.3.2 The Risk Universe 1.3.2.1 Business Risk 1.3.2.2 Risk by Nature 1.3.2.3 Rumsfeld Matrix 1.3.2.4 Black Swans and Other Strange Creatures 1.3.3 Measure 1.3.3.1 Assess, Measure, Communicate 1.3.3.2 Time Horizons 1.3.3.3 Desired Rating Properties 1.3.4 Beware of Fallacies 1.3.4.1 Argument 1.3.4.2 Evidence 1.3.4.3 Appeals 1.4 The Moneylender 1.4.1 Credit’s 5 Cs 1.4.2 Borrowings and Structure 1.4.3 Engagement 1.4.3.1 Relationship Lending 1.4.3.2 Transactional 1.4.3.3 Providers—Benefits or Not 1.4.3.4 Customers—Benefits or Not 1.4.4 Retail versus Wholesale 1.4.4.1 Wholesale 1.4.4.2 Retail 1.4.4.3 Grade Presentation 1.4.4.4 From Rules and Judgment to Models 1.4.4.5 Empirical Ratings 1.4.5 Risk-Based Pricing (RBP) 1.5 Summary 2: Predictive Modelling Overview 2.1 Models 2.1.1 Model Types and Uses 2.1.2 Choices and Elements 2.1.2.1 Functional Form 2.1.2.2 Methodology 2.1.2.3 Parameter Estimation 2.1.3 Model Lifecycle 2.2 Model Risk (MR) 2.2.1 Categories 2.2.2 Management 2.2.3 Models on Models 2.3 Shock Events 2.3.1 Past Events 2.3.2 COVID-19 Views 2.4 Data 2.4.1 Desired Qualities 2.4.2 Sources 2.4.2.1 Homophily 2.4.3 Types 2.4.3.1 Honouring Obligations 2.4.3.2 Transactions 2.4.3.3 Non-Financial Behaviour 2.4.3.4 Disclosed 2.4.3.5 Investigation 2.4.3.6 Providing Comfort 2.5 Summary 3: Retail Credit 3.1 Scorecard Terminology 3.1.1 Targeting Rare Events 3.1.2 Functional Forms 3.2 Retail Model Types 3.2.1 When?—Credit Risk Management Cycle 3.2.2 What?—The Four Rs of Customer Measurement 3.2.3 Who?—Experience, To Borrow or Not To Borrow 3.2.4 How?—Empirical versus Judgment 3.2.5 The Commonest Types 3.3 Data Sources 3.3.1 Credit Bureaux 3.3.2 Ownership Types 3.3.3 Credit Registries 3.4 Risk ‘Indicators’ 3.4.1 Types of Risk Indicators 3.4.2 Banding Presentation 3.5 FICO Scores 3.5.1 Scaling Parameters 3.6 Summary 4: Business Credit 4.1 Risk 101 4.1.1 Credit Risk Analysis 4.1.2 Data Sources 4.1.3 Risk Assessment Tools 4.1.4 Rating Grades 4.1.4.1 External Grades 4.1.4.2 Standard & Poor’s (S&P) 4.1.4.3 Internal Grades 4.1.4.4 Master Rating Scales 4.1.5 SME (Small and Medium Enterprises) Lending 4.2 Financial Ratio Scoring 4.2.1 Pioneers 4.2.2 Predictive Ratios 4.2.3 Agency Usage 4.2.4 Moody’s RiskCalcTM 4.2.5 Non-Financial Factors 4.3 Use of Forward-Looking Data 4.3.1 Historical Analysis 4.3.2 Structural Models 4.3.3 Reduced-Form Models 4.3.3.1 Credit Spreads 4.4 Summary Module B: The Histories 5: Side Histories 5.1 The Industrial Revolutions 5.1.1 Authors and Players 5.1.2 Further Details 5.1.3 Implications 5.2 Booms and Busts; Bubbles and Bursts 5.2.1 17th Century 5.2.2 18th Century 5.2.3 19th Century 5.2.3.1 5.2.3.2 (1857–59) Panic of 1857 5.2.4 (1873–96) The Long Depression 5.2.5 20th Century 5.2.6 21st Century 5.3 Registration 5.3.1 Social Relationships 5.3.1.1 Hierarchies 5.3.1.2 Obligations 5.3.2 In History 5.3.2.1 Ancient China 5.3.2.2 Ancient Rome 5.3.2.3. Early-Modern Europe 5.3.3 Evidence 5.3.3.1 Passports 5.3.3.2 References 5.3.3.3 Certificates 5.3.3.4 Tokens 5.4 Identification 5.4.1 Visual 5.4.1.1 Traces 5.4.1.2 Worn and Borne 5.4.1.3 Mannerisms 5.4.2 Oral 5.4.2.1 Voice 5.4.2.2 Language and Accent 5.4.2.3 Unique Knowledge 5.4.3 Disclosed 5.4.3.1 Names 5.4.3.2 Numbers 5.4.4 Authenticators 5.4.5 Invasive 5.5 Summary 6: Credit—A Microhistory 6.1 The Ancient World 6.1.1 Mesopotamia 6.1.2 Greece 6.1.3 Roman Empire 6.2 The Mediaeval World 6.2.1 Early Middle Ages 6.2.2 Churches and Holy Men 6.2.3 Pawnbroking 6.2.4 Vifgage and Morgage 6.2.5 Merchant Banking 6.2.6 Bankruptcy Legislation—16th through 18th Centuries 6.3 Credit Evolution 6.3.1 Trade Finance and Investment 6.3.2 Personal Credit—Pre-1880 6.3.3 Personal Credit—1880s Onwards 6.3.4 Instalment Credit 6.4 Credit Vendors 6.4.1 Tallymen, Credit Drapers and Travelling Salesmen 6.4.2 Department Stores 6.4.3 Mail Order 6.4.4 Mobile Network Operators (MNO)s 6.4.5 Internet Service Providers 6.5 Credit Media and Assets Financed 6.5.1 Promissory Note and Bill of Exchange 6.5.2 Cheques and Overdrafts 6.5.2.1 Cheques 6.5.2.2 Overdrafts 6.5.3 Charge and Credit Cards 6.5.4 Car Loans and Consumer Durables 6.5.5 Home Loans 6.5.6 Student Loans 6.6 Summary and Reflections Questions—History of Credit 7: The Birth of Modern Credit Intelligence 7.1 Pre-Revolution 7.2 United Kingdom 7.3 United States 7.3.1 Early America 7.3.1.1 Mercantile Reporting Agencies 7.3.1.2 The Tappans 7.3.1.3 Mercantile to 1859 7.3.1.4 Dun versus Bradstreet to 1933 7.3.1.5 19th-Century Operations 7.3.1.6 Publishing and Rating 7.3.1.7 Dun & Bradstreet 7.3.2 Credit Men and Information Exchanges 7.3.3 Credit Bureaux 7.4 The ‘Big Three’ Credit Bureaux, Plus Some 7.4.1 Equifax 7.4.2 Experian 7.4.2.1 Commercial Credit Nottingham (UK) 7.4.2.2 Thompson Ramo Wooldridge (USA) 7.4.2.3 Experian 7.4.3 TransUnion 7.4.4 Centrale Rischi Finanziari (CRIF) 7.4.5 CreditInfo 7.4.6 Others 7.4.7 Current Spread 7.4.8 Economics and Statistics 7.5 Rating Agencies 7.6 High-Level Observations 7.7 Summary and Reflections Questions—History of Credit Intelligence 8: The Dawn of Credit Scoring 8.1 Before Statistics 8.2 Statistical Experiments: 1941–1958 8.3 Rise of the Scorecard Vendor 8.3.1 Fair, Isaac & Co. (FICO) 8.3.2 VantageScore Solutions 8.3.3 Management Decision Systems (MDS) 8.3.4 Scorelink and Scorex 8.4 Rise of the Corporate Modeller 8.4.1 JP Morgan 8.4.2 Kealhofer McQuown Vašícek (KMV) 8.4.3 Moody’s Analytics 8.5 Regulation 8.5.1 Privacy—Fair Credit Reporting Act (FCRA) (1970) 8.5.2 Privacy—OECD and European Legislation 8.5.3 Anti-Discrimination Equal Credit Opportunity Act 8.5.4 Capital Requirements—Basel II, III, IV 8.5.5 Accounting—International Financial Reporting Standards (IFRS) 8.6 Borrowed Concepts 8.7 Statistical Methods 8.7.1 Linear Programming (FICO) 8.7.2 Discriminant Analysis 8.7.3 Linear Probability Modelling (LPM) 8.7.4 Logistic Regression (Independents and Others) 8.7.5 Neural Networks 8.7.6 Other Non-Parametric Techniques 8.8 Summary and Reflections Questions—History of Credit Scoring Module C: Credit Lifecycle Shared Service Centres (SSCs) 9: Front-Door 9.1 Marketing 9.1.1 Advertising 9.1.2 Two Tribes 9.1.3 Pre-Screening 9.1.4 Data 9.1.5 Summary Questions—Marketing 9.2 Origination 9.2.1 Gather—Interested Customer Details 9.2.1.1 Acquire Applicant Details 9.2.1.2 Paper-based capture 9.2.1.3 Pre-scoring screening and sanitation 9.2.2 Sort—Into Strategy Buckets 9.2.2.1 Enquire—Internal 9.2.2.2 Enquire—External 9.2.2.3 Measure and Decide 9.2.3 Action—Accept or Reject 9.2.3.1 Declines 9.2.3.2 Accepts 9.2.4 Summary Questions—Originations 9.3 Account Management 9.3.1 Types of Limits 9.3.1.1 Use of Other Scores 9.3.1.2 Triage 9.3.2 Over-Limit Management (Takers) 9.3.2.1 Cheque Accounts—Pay/No Pay 9.3.2.2 Credit Cards—Authorizations 9.2.3.3 Informed Customer Effect 9.3.3 More Limit and Other Functions 9.3.3.1 Limit-Increase Requests (Askers) 9.3.3.2 Limit-Increase Campaigns (Givers) 9.3.3.3 Limit Reviews (Repeaters) 9.3.3.4 Cross-Sales (Repayers/Repeaters/Leavers) 9.3.3.5 Win-Back (Leavers) 9.3.4 Summary Questions—Account Management 10: Back-Door 10.1 Collections and Recoveries (C&R) 10.1.1 Overview 10.1.1.1 Delinquency Reasons 10.1.1.2 Excuses 10.1.2 Process 10.1.2.1 Core Systems Requirements 10.1.2.2 Agencies 10.1.2.3 Reporting 10.1.3 Triggers and Strategies 10.1.3.1 Strategy Setting 10.1.3.2 Practical Considerations 10.1.4 Modelling 10.1.4.1 Collections and Recoveries (C&R) versus Behavioural 10.1.4.2 Collections Scorecard Classifications 10.1.4.3 Champion/Challenger 10.1.5 Summary Questions—Collections 10.2 Fraud 10.2.1 Credit Card Fraud Trends 10.2.2 Definitions 10.2.2.1 Relationship to Account 10.2.2.1.1 Kite Flying 10.2.2.2 Misrepresentation 10.2.2.2.1 Embellishment/Massaging 10.2.2.2.2 Social Engineering 10.2.2.2.3 Identity Theft and Synthetic Identities 10.2.2.2.4 Property Hijacking 10.2.2.2.5 Advanced Persistent Threat 10.2.2.3 Authorization 10.2.2.3.1 Unauthorized Fraud 10.2.2.3.2 Authorized Fraud 10.2.3 Prevention Measures 10.2.3.1 Manual/Physical Measures 10.2.3.2 Online/Telephonic Measures 10.2.3.2.1 Knowledge 10.2.3.2.2 Visual Biometrics 10.2.3.2.3 Other Biometrics 10.2.3.2.4 Tokens 10.2.3.2.5 Multi-Factor 10.2.4 Data and Tools 10.2.5 Summary Questions—Fraud Module D: Toolbox 11: Stats & Maths & Unicorns 11.1 Variance and Correlations 11.1.1 Variance 11.1.2 Pairwise Correlations 11.1.2.1 Causation versus Coincidence 11.1.2.2 Why are Correlations an Issue? 11.1.2.3 Measures and Thresholds 11.1.2.4 Variable Types 11.1.3 Pearson’s Product-Moment 11.1.4 Spearman’s Rank-Order 11.1.5 Mahalanobis Distance 11.1.6 Variance Inflation Factor (VIF) 11.1.6.1 Greek, Damn Greek and Statistics 11.2.1 Coefficient of Determination (R-squared 11.2 Goodness-of Fit Tests 11.2.1 Coefficient of Determination (R-squared) 11.2.2 Pearson’s Chi-Square 11.2.3 Hosmer–Lemeshow Statistic 11.3 Likelihood 11.3.1 Log-Likelihood 11.3.2 Deviance 11.3.3 Akaike Information Criterion (AIC) 11.3.4 Bayesian Information Criterion (BIC) 11.4 Holy Trinity 11.4.1 Likelihood Ratio 11.4.1.1 Wilks’ Theorem 11.4.2 Wald Chi-Square 11.4.3 Rao’s Score Chi-Square 11.5 Summary Questions—Stats & Maths & Unicorns 12: Borrowed Measures 12.1 Mathematics and Probability Theory 12.1.1 Logarithms 12.1.1.1 Archimedes 12.1.1.2 Napier, Briggs and Bürgi 12.1.1.3 Bernoulli and Euler 12.1.2 Laws of Large Numbers 12.1.3 Bayes’ Theorem 12.1.4 Laplace—Expected Values 12.1.5 Kolmogorov–Smirnov—Curve and Statistic 12.1.6 Gradient Descent 12.2 Probability Distributions and Hypotheses 12.2.1 Binomial Distribution 12.2.2 Normal Distribution and Z-Scores 12.2.3 Student’s t-Distribution 12.2.4 Verhulst’s Logistic Curve 12.2.5 Pearson’s Chi-Square Distribution 12.3 Economics 12.3.1 Lorenz Curve 12.3.2 Gini Coefficient 12.3.3 Gini Impurity Index 12.4 Information Theory and Cryptography 12.4.1 Shannon’s Entropy 12.4.2 Gudak—Weight of Evidence (WoE) 12.4.3 Kullback—Divergence Statistic 12.5 Signal-Detection Theory 12.5.1 Confusion Matrices 12.5.2 Receiver Operating Characteristic (ROC) 12.5.3 Area under the ROC (AUROC or AUC) 12.6 Forecasting 12.6.1 Markov Chains 12.6.2 Survival Analysis 12.7 Summary Questions—Borrowed Measures 13: Practical Application 13.1 Characteristic Transformations 13.1.1 Rescale 13.1.2 Discretize 13.1.2.1 Dummy Variables 13.1.2.2 Piecewise 13.1.2.3 Weight of Evidence (WoE) 13.2 Characteristic Assessments 13.2.1 Information Value (IV) 13.2.2 Population Stability Index (PSI) 13.2.3 Chi-Square (.2) 13.3 Model Assessments 13.3.1 Lorenz and Gini 13.3.2 Cumulative Accuracy Profile, Accuracy Ratio and Lift 13.3.3 Divergence Statistic 13.4 Odds and Sods 13.4.1 Deviance Odds 13.4.2 Calinski–Harabasz Statistic 13.4.3 Gini Variance 13.5 Summary Questions—Power, Separation and Accuracy 14: Predictive Modelling Techniques 14.1 A View from on High! 14.1.1 Caveats 14.1.2 Learning the Language 14.2 Parametric 14.2.1 Linear Regression 14.2.2 Discriminant Analysis 14.2.3 Linear Probability Modelling (LPM) 14.2.4 Probability Unit (Probit) 14.2.5 Logistic Regression (Logit) 14.2.6 Linear Programming 14.2.6.1 LP for Classification 14.3 Non-Parametric 14.3.1 K-Nearest Neighbours 14.3.2 Decision Trees 14.3.2.1 Bootstrap Aggregation and Random Forests (RF)s 14.3.3 Support Vector Machines (SVM) 14.3.4 Artificial Neural Networks 14.3.5 Genetic Algorithms 14.4 Conglomerations 14.4.1 Multiple Models 14.4.1.1 Practical 14.14.1.2 Parallel 14.4.1.3 Sequential 14.4.2 Machine Learning 14.5 Making the Choice 14.6 Summary QUESTIONS—Predictive Modelling Techniques Module E: Organizing 15: Project Management 15.1 Development Process Overview 15.1.1 Initiation 15.1.2 Preparation 15.1.3 Construction 15.1.4 Finalization 15.2 Initiation and ‘Project Charter’ 15.2.1 High-Level 15.2.1.1 Model register 15.2.2 Making the Case 15.2.2.1 Business Case 15.2.2.2 Scope 15.2.2.3 Assessment Criteria 15.2.3 Stakeholders and Players 15.2.3.1 Sponsor and Steering Committee 15.2.3.2 Project Manager (PM) 15.2.3.3 Team Lead (TL) 15.2.3.4 Team Members 15.2.4 Resources and Timetables 15.2.4.1 Resources 15.2.4.2 Project Timetable 15.2.5 Assumptions, Risks and Constraints 15.2.5.1 Target Specification 15.2.5.2 Model Form 15.2.5.3 Data Sources 15.2.5.4 Environmental Instability 15.3 Project Deliverables 15.3.1 Communication and Documentation 15.3.2 Model Development Documentation (MDD) 15.3.3 Implementation Instructions (MIID) 15.3.4 Project Code 15.3.5 Data 15.4 Other Considerations 15.4.1 Scorecard Development Software 15.4.1.1 Statistical Analysis System (SAS) 15.4.1.2 World Programming System (WPS) Analytics 15.4.1.3 Statistical Package for the Social Sciences (SPSS) 15.4.1.4 R 15.4.1.5 Python 15.4.1.6 Other Proprietary Tools 15.4.2 Implementation 15.4.2.1 Decision-Making Stages 15.4.2.2 Technology Options 15.4.2.3 Vendors 15.4.3 Next Steps 15.5 Summary 16: Data Acquisition—Observation 16.1 Make a Plan! 16.2 Gather 16.2.1 Key Fields 16.2.2 Matching Keys 16.2.3 Data Aggregation 16.2.4 Retention Rules 16.2.4.1 Retrospective Histories 16.3 Reduce 16.3.1 Characteristic Review 16.3.2 Proscribed Characteristics 16.3.3 Unand Under-Populated Characteristics 16.3.4 Correlated Characteristics 16.4 Cleanse 16.4.1 Out-of Scope 16.4.2 Underpopulated 16.4.3 Duplicates 16.4.4 Outliers 16.4.5 Inconsistencies 16.5 Check 16.6 Summary 17: Data Acquisition—Performance 17.1 Planning Extraction 17.1.1 Minimum Requirements 17.1.2 Casting the Net 17.1.3 Basic Checks 17.2 File Preparation and Review 17.2.1 Deep Dives of Simple Sorts 17.2.2 Performance Arrays 17.2.3 Payment Profile Strings 17.2.4 Performance Maintenance 17.3 Window Setting 17.3.1 Length 17.3.2 End-of-versus Worst-of-Window 17.3.3 Fixed vs Variable 17.4 Summary 18: Target Definition 18.1 Overview 18.1.1 Binaries 18.1.2 Requirements 18.1.3 Performance Components 18.1.4 Code Crosschecks 18.2 Definition Strictness 18.2.1 Status nodes 18.2.2 Level of Delinquency 18.2.2.1 Time 0 vs Time 1 18.2.2.2 Time 1 vs Time 2 18.2.3 Trivial Balances 18.2.4 Closed Accounts 18.3 Integrity Checks 18.3.1 Consistency Check 18.3.2 Characteristic Check 18.3.3 Swap-Set Check 18.4 Summary 19: File Assembly 19.1 Merge Observation and Performance 19.1.1 Finding Performance 19.1.2 Outcome Field Merge 19.1.3 Kill and Other Rules 19.1.4 Not Taken Up (NTU), Uncashed 19.2 External Data Acquisition 19.2.1 Retro History Requests 19.2.2 Data Security 19.3 Further Reduction 19.3.1 Pre-Processing 19.3.2 Correlated Characteristics 19.3.2.1 Weak Characteristics 19.4 Summary Module F: Packing 20: Sample Selection 20.1 Overview 20.1.1 Terminology 20.1.2 Optimal and Minimum Sample Sizes 20.1.3 Law of Diminishing Data Returns 20.2 Training, Holdout, Out-of Time Recent (THOR) Samples 20.2.1 Sample Types 20.2.2 Sampling Guidelines 20.2.2.1 Training 20.2.2.2 Hold-out 20.2.2.3 Out-of Time 20.2.2.4 Recent 20.2.3 Observation Windows 20.2.4 Sampling Plan and Outcome 20.3 Afterthoughts 20.3.1 Unand Under-Populated Characteristics 20.3.2 Exact Random Sample 20.3.3 Housekeeping 20.4 Summary 21: Data Transformation 21.1 Traditional Transformations 21.1.1 Dummy Variables 21.1.2 Weight of Evidence 21.1.3 Piecewise 21.2 Classing/Binning 21.2.1 Characteristic Analysis Reports 21.2.2 Bulk Classing 21.2.3 Fine Classing 21.2.4 Coarse Classing 21.2.4.1 Class Sizes 21.2.4.2 Monotonicity 21.2.4.3 Automation 21.2.4.4 Training versus Hold-Out and Out-of Time 21.2.4.5 Known versus Inferred 21.2.4.6 Final Checks 21.2.5 Piecewise Classing 21.2.6 Final Transformation 21.3 Missing Data Treatment 21.3.1 Traditional 21.3.2 Missing Singles 21.3.3 Missing Multiples 21.4 Summary Questions—Data Transformation 22: Segmentation 22.1 Overview 22.1.1 Drivers 22.1.1.1 Common Splits 22.1.2 Inhibitors 22.1.3 Mitigators 22.2 Analysis 22.2.1 Learning Types 22.2.2 Finding Interactions Open versus closed form Open form for binaries Closed form for binaries 22.2.3 Segment Mining 22.2.4 Boundary Analysis 22.2.4.1 Boundary Types 22.2.4.2 An Example 22.3 Presentation 22.4 Summary Questions—Segmentation 23: Reject-Inference 23.1 The Basics 23.1.1 Pointers 23.1.2 Missing at Random, or Not 23.1.3 Terminology 23.1.4 Characteristic Analysis 23.1.5 Swap-SetAnalysis 23.1.5.1 Score Level 23.1.5.2 Characteristic Level 23.1.6 Population-FlowDiagram 23.2 Intermediate Models 23.2.1 Accept/Reject 23.2.2 Taken Up/Not Taken Up (TU/NTU) 23.2.3 Known Good/Bad 23.2.4 Bringing it All Together 23.3 The Inference Smorgasbord 23.3.1 Supplementation 23.3.2 Performance Surrogates 23.3.2.1 It’s a Bird, it’s a Plane; no, it’s . . . Super Model! 23.3.3 Reject Equals Bad 23.3.4 Augmentation 23.3.5 Weight of Evidence (WoE) Adjustments 23.3.6 Iterative Reclassification 23.3.7 Extrapolation 23.4 Favoured Technique 23.4.1 Fuzzy-Parcelling 23.4.2 Extrapolation 23.4.2.1 Raw Inference 23.4.2.2 Extra Prejudice 23.4.3 Attribute-Level Adjustments 23.5 Let’s Get Practical! 23.5.1 Variable Names and Codes 23.5.2 Record-Level Inference Example 23.6 Summary Questions—Reject-Inference Module G: Making the Trip 24: Model Training 24.1 Regression 24.1.1 Options and Settings 24.1.2 Regression Outputs 24.2 Variable Selection 24.2.1 Criteria 24.2.2 Automated Variable Selection (AVS) 24.2.3 Stepwise Output Review 24.2.3.1 Stepping Summary 24.2.3.2 Model Coefficients 24.2.4 Constraining the Beta Beast 24.2.4.1 Negative Coefficients—Beta < 0 24.2.4.2 Overprediction—Beta > 1 24.2.5 Stepping by Gini 24.3 Correlation and Multi-Collinearity 24.3.1 Multi-Collinearity 24.3.2 Pairwise Correlations 24.4 Blockwise Variable Selection 24.4.1 Variable Reduction Blocks 24.4.2 Staged Blocks (Residual Prediction) 24.4.3 Embedded Blocks 24.4.4 Ensemble Blocks 24.5 Multi-ModelComparisons 24.5.1 Lorenz Curve Comparisons 24.5.2 Strategy Curve Comparisons 24.6 Model Calibration 24.6.1 Simple Calibration 24.6.2 Piecewise Calibration 24.6.3 Score and Points CalibrationThe previous section assumes we are adjusting predictions provided 24.6.4 MAPA Calibration 24.7 Summary Questions—Model Training 25: Scaling and Banding 25.1 Scorecard Scaling 25.1.1 Background 25.1.2 Percentages 25.1.3 Fixed Ranges 25.1.4 Scaling Parameters 25.1.4.1 Basic Formulae 25.1.4.2 Intercepts and Coefficients → Scorecard Points 25.1.4.3 Log-Odds↔ Probability ↔ Score 25.1.5 Other Considerations 25.1.5.1 Scorecard Presentation 25.1.5.2 Adverse Reason Codes 25.2 Risk Banding 25.2.1 Zero Constraints 25.2.2 Fitted Distributions 25.2.3 Benchmarked 25.2.4 Fixed-BandBoundaries 25.3 Summary Questions—Scaling and Banding 26: Finalization 26.1 Validation 26.1.1 High Level 26.1.2 Independent Oversight 26.1.3 Quantitative Assessment 26.1.4 Assessing Misalignment 26.1.4.1 Score Misalignments 26.1.4.2 Characteristic Misalignment 26.2 Documentation 26.2.1 Possible Outline 26.2.2 Supplementary Tables and Graphics 26.2.2.1 Model Development 26.2.2.2 Period-On-Period—Pre- and Post-implementation 26.2.3 Selection Strategies 26.2.3.1 Failure versus Rejection—That is the Question! 26.2.3.2 Preparing for Post-implementation 26.2.4 Comparing New Against Old 26.2.4.1 Rating Transition Matrix 26.2.4.2 Swap Sets 26.3 Implementation 26.3.1 Platform Choice 26.3.1.1 Criticality 26.3.1.2 Budget 26.3.2 Testing 26.3.3 Further Considerations 26.4 Monitoring 26.4.1 Front-End 26.4.1.1 Overrides 26.4.2 Back-End 26.4.2.1 Vintage/Cohort Analysis 26.4.2.2 Early Monitoring 26.5 Summary Questions—Finalization Afterword Areas for Further Research Societal and Histories Empirical Module Z: Appendices Glossary Bibliography Index