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.
By:
Naeem Siddiqi
Imprint: John Wiley & Sons Inc
Country of Publication: United States
Edition: 2nd edition
Dimensions:
Height: 231mm,
Width: 160mm,
Spine: 43mm
Weight: 680g
ISBN: 9781119279150
ISBN 10: 1119279151
Series: Wiley and SAS Business Series
Pages: 464
Publication Date: 30 December 2016
Audience:
Professional and scholarly
,
Undergraduate
Format: Hardback
Publisher's Status: Active
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
NAEEM SIDDIQI is the Director of Credit Scoring and Decisioning with SAS® Institute. He has more than twenty years of experience in credit risk management, both as a consultant and as a user at financial institutions. He played a key role in developing SAS® Credit Scoring and continues to provide worldwide support for the initiative.
