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Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards, 2nd Edition



Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards, 2nd Edition

Naeem Siddiqi

ISBN: 978-1-119-28233-4 December 2016 464 Pages


A better development and implementation framework for credit risk scorecards

Intelligent Credit Scoring presents a business-oriented process for the development and implementation of risk prediction scorecards. The credit scorecard is a powerful tool for measuring the risk of individual borrowers,  gauging overall risk exposure and developing analytically driven, risk-adjusted strategies for existing customers. In the past 10 years, hundreds of banks worldwide have brought the process of developing credit scoring models in-house, while ‘credit scores' have become a frequent topic of conversation in many countries where bureau scores are used broadly. In the United States, the ‘FICO' and ‘Vantage' scores continue to be discussed by borrowers hoping to get a better deal from the banks. While knowledge of the statistical processes around building credit scorecards is common, the business context and intelligence that allows you to build better, more robust, and ultimately more intelligent, scorecards is not. As the follow-up to Credit Risk Scorecards, this updated second edition includes new detailed examples, new real-world stories, new diagrams, deeper discussion on topics including WOE curves, the latest trends that expand scorecard functionality and new in-depth analyses in every chapter. Expanded coverage includes new chapters on defining infrastructure for in-house credit scoring, validation, governance, and Big Data.

Black box scorecard development by isolated teams has resulted in statistically valid, but operationally unacceptable models at times. This book shows you how various personas in a financial institution can work together to create more intelligent scorecards, to avoid disasters, and facilitate better decision making. Key items discussed include:

  • Following a clear step by step framework for development, implementation, and beyond
  • Lots of real life tips and hints on how to detect and fix data issues
  • How to realise bigger ROI from credit scoring using internal resources
  • Explore new trends and advances to get more out of the scorecard

Credit scoring is now a very common tool used by banks, Telcos, and others around the world for loan origination, decisioning, credit limit management, collections management, cross selling, and many other decisions. Intelligent Credit Scoring helps you organise resources, streamline processes, and build more intelligent scorecards that will help achieve better results.

Acknowledgments xiii

Chapter 1 Introduction 1

Scorecards: General Overview 9

Notes 18

Chapter 2 Scorecard Development: The People and the Process 19

Scorecard Development Roles 21

Intelligent Scorecard Development 31

Scorecard Development and Implementation Process: Overview 31

Notes 34

Chapter 3 Designing the Infrastructure for Scorecard Development 35

Data Gathering and Organization 39

Creation of Modeling Data Sets 41

Data Mining/Scorecard Development 41

Validation/Backtesting 43

Model Implementation 43

Reporting and Analytics 44

Note 44

Chapter 4 Scorecard Development Process, Stage 1: Preliminaries and Planning 45

Create Business Plan 46

Create Project Plan 57

Why “Scorecard” Format? 60

Notes 61

Chapter 5 Managing the Risks of In-House Scorecard Development 63

Human Resource Risk 65

Technology and Knowledge Stagnation Risk 68

Chapter 6 Scorecard Development Process, Stage 2: Data Review and Project Parameters 73

Data Availability and Quality Review 74

Data Gathering for Definition of Project Parameters 77

Defi nition of Project Parameters 78

Segmentation 103

Methodology 116

Review of Implementation Plan 117

Notes 118

Chapter 7 Default Definition under Basel 119

Introduction 120

Default Event 121

Prediction Horizon and Default Rate 124

Validation of Default Rate and Recalibration 126

Application Scoring and Basel II 128

Summary 129

Notes 130

Chapter 8 Scorecard Development Process, Stage 3: Development Database Creation 131

Development Sample Specification 132

Sampling 140

Development Data Collection and Construction 142

Adjusting for Prior Probabilities 144

Notes 148

Chapter 9 Big Data: Emerging Technology for Today’s Credit Analyst 149

The Four V’s of Big Data for Credit Scoring 150

Credit Scoring and the Data Collection Process 158

Credit Scoring in the Era of Big Data 159

Ethical Considerations of Credit Scoring in the Era of Big Data 164

Conclusion 170

Notes 171

Chapter 10 Scorecard Development Process, Stage 4: Scorecard Development 173

Explore Data 175

Missing Values and Outliers 175

Correlation 178

Initial Characteristic Analysis 179

Preliminary Scorecard 200

Reject Inference 215

Final Scorecard Production 236

Choosing a Scorecard 246

Validation 258

Notes 262

Chapter 11 Scorecard Development Process, Stage 5: Scorecard Management Reports 265

Gains Table 267

Characteristic Reports 273

Chapter 12 Scorecard Development Process, Stage 6: Scorecard Implementation 275

Pre-implementation Validation 276

Strategy Development 291

Notes 318

Chapter 13 Validating Generic Vendor Scorecards 319

Introduction 320

Vendor Management Considerations 323

Vendor Model Purpose 326

Model Estimation Methodology 331

Validation Assessment 337

Vendor Model Implementation and Deployment 340

Considerations for Ongoing Monitoring 341

Ongoing Quality Assurance of the Vendor 351

Get Involved 352

Appendix: Key Considerations for Vendor Scorecard Validations 353

Notes 355

Chapter 14 Scorecard Development Process, Stage 7: Post-implementation 359

Scorecard and Portfolio Monitoring Reports 360

Reacting to Changes 377

Review 399

Notes 401

Appendix A: Common Variables Used in Credit Scoring 403

Appendix B: End-to-End Example of Scorecard Creation 411

Bibliography 417

About the Author 425

About the Contributing Authors 427

Index 429