NAEEM SIDDIQI is the Director of Credit Scoring and Decisioning with SAS(R) 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(R) Credit Scoring and continues to provide worldwide support for the initiative.

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
 
Chapte

Lieferung vom Verlag mit leichten Qualitätsmängeln möglich

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.