Intelligent Credit Scoring

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.

A better development and implementation framework for credit risk scorecardsIntelligent 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 scorecardCredit 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 xiiiChapter 1 Introduction 1Scorecards: General Overview 9Notes 18Chapter 2 Scorecard Development: The People and the Process 19Scorecard Development Roles 21Intelligent Scorecard Development 31Scorecard Development and Implementation Process: Overview 31Notes 34Chapter 3 Designing the Infrastructure for Scorecard Development 35Data Gathering and Organization 39Creation of Modeling Data Sets 41Data Mining/Scorecard Development 41Validation/Backtesting 43Model Implementation 43Reporting and Analytics 44Note 44Chapter 4 Scorecard Development Process, Stage 1: Preliminaries and Planning 45Create Business Plan 46Create Project Plan 57Why "Scorecard" Format? 60Notes 61Chapter 5 Managing the Risks of In-House Scorecard Development 63Human Resource Risk 65Technology and Knowledge Stagnation Risk 68Chapter 6 Scorecard Development Process, Stage 2: Data Review and Project Parameters 73Data Availability and Quality Review 74Data Gathering for Definition of Project Parameters 77Defi nition of Project Parameters 78Segmentation 103Methodology 116Review of Implementation Plan 117Notes 118Chapter 7 Default Definition under Basel 119Introduction 120Default Event 121Prediction Horizon and Default Rate 124Validation of Default Rate and Recalibration 126Application Scoring and Basel II 128Summary 129Notes 130Chapter 8 Scorecard Development Process, Stage 3: Development Database Creation 131Development Sample Specification 132Sampling 140Development Data Collection and Construction 142Adjusting for Prior Probabilities 144Notes 148Chapter 9 Big Data: Emerging Technology for Today's Credit Analyst 149The Four V's of Big Data for Credit Scoring 150Credit Scoring and the Data Collection Process 158Credit Scoring in the Era of Big Data 159Ethical Considerations of Credit Scoring in the Era of Big Data 164Conclusion 170Notes 171Chapter 10 Scorecard Development Process, Stage 4: Scorecard Development 173Explore Data 175Missing Values and Outliers 175Correlation 178Initial Characteristic Analysis 179Preliminary Scorecard 200Reject Inference 215Final Scorecard Production 236Choosing a Scorecard 246Validation 258Notes 262Chapter 11 Scorecard Development Process, Stage 5: Scorecard Management Reports 265Gains Table 267Characteristic Reports 273Chapter 12 Scorecard Development Process, Stage 6: Scorecard Implementation 275Pre-implementation Validation 276Strategy Development 291Notes 318Chapter 13 Validating Generic Vendor Scorecards 319Introduction 320Vendor Management Considerations 323Vendor Model Purpose 326Model Estimation Methodology 331Validation Assessment 337Vendor Model Implementation and Deployment 340Considerations for Ongoing Monitoring 341Ongoing Quality Assurance of the Vendor 351Get Involved 352Appendix: Key Considerations for Vendor Scorecard Validations 353Notes 355Chapter 14 Scorecard Development Process, Stage 7: Post-implementation 359Scorecard and Portfolio Monitoring Reports 360Reacting to Changes 377Review 399Notes 401Appendix A: Common Variables Used in Credit Scoring 403Appendix B: End-to-End Example of Scorecard Creation 411Bibliography 417About the Author 425About the Contributing Authors 427Index 429