Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards (Wiley and SAS Business Series)

**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 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. Intelligent Credit Scoring -- Contents -- Acknowledgments -- Chapter 1 Introduction -- Scorecards: General Overview -- Notes -- Chapter 2 Scorecard Development: The People and the Process -- Scorecard Development Roles -- Scorecard Developer -- Data Scientist -- Product or Portfolio Risk Manager/Credit Scoring Manager -- Product Manager(s) -- Operational Manager(s) -- Model Validation/Vetting Staff -- Project Manager -- IT/IS Managers -- Enterprise Risk/Corporate Risk Management Staff (Where Applicable) -- Legal Staff/Compliance Manager -- Intelligent Scorecard Development -- Scorecard Development and Implementation Process: Overview -- Notes -- Chapter 3 Designing the Infrastructure for Scorecard Development -- Data Gathering and Organization -- Creation of Modeling Data Sets -- Data Mining/Scorecard Development -- Validation/Backtesting -- Model Implementation -- Reporting and Analytics -- Note -- Chapter 4 Scorecard Development Process, Stage 1: Preliminaries and Planning -- Create Business Plan -- Identify Organizational Objectives and Scorecard Role -- Internal versus External Development and Scorecard Type -- Create Project Plan -- Identify Project Risks -- Identify Project Team -- Why "Scorecard" Format? -- Notes -- Chapter 5 Managing the Risks of In-House Scorecard Development -- Human Resource Risk -- Technology and Knowledge Stagnation Risk -- Chapter 6 Scorecard Development Process, Stage 2: Data Review and Project Parameters -- Data Availability and Quality Review -- Data Gathering for Definition of Project Parameters -- Definition of Project Parameters -- Exclusions -- Performance and Sample Windows and Bad Definition -- Effects of Seasonality -- Definition of Bad -- Dealing with Low-Default Portfolios -- Confirming the Bad Definition -- Good and Indeterminate -- Segmentation -- Experience-Based (Heuristic) Segmentation Statistically Based Segmentation -- Comparing Improvement -- Choosing Segments -- Methodology -- Review of Implementation Plan -- Notes -- Chapter 7 Default Definition under Basel -- Introduction -- Default Event -- Prediction Horizon and Default Rate -- Validation of Default Rate and Recalibration -- Application Scoring and Basel II -- Summary -- Notes -- Chapter 8 Scorecard Development Process, Stage 3: Development Database Creation -- Development Sample Specification -- Selection of Characteristics -- Sampling -- Development/Validation -- Good/Bad/Reject -- Development Data Collection and Construction -- Random and Representative -- Nonsegmented Data Set -- Data Quirks -- Adjusting for Prior Probabilities -- Offset Method -- Sampling Weights -- Notes -- Chapter 9 Big Data: Emerging Technology for Today's Credit Analyst -- The Four V's of Big Data for Credit Scoring -- Volume -- Velocity -- Variety -- Value -- Credit Scoring and the Data Collection Process -- Credit Scoring in the Era of Big Data -- The Promise of Big Data for Banks -- Population versus Sample in the World of Big Data -- Ethical Considerations of Credit Scoring in the Era of Big Data -- The Rise of "Big Brother" Issues When Using Big Data -- Conclusion -- Notes -- Chapter 10 Scorecard Development Process, Stage 4: Scorecard Development -- Explore Data -- Missing Values and Outliers -- Correlation -- Initial Characteristic Analysis -- Statistical Measures -- Logical Trend -- Business/Operational Considerations -- Preliminary Scorecard -- Risk Profile Concept -- Logistic Regression -- Designing a Scorecard -- Reject Inference -- Reasons for Reject Inference -- Reject Inference Techniques -- Verification -- Final Scorecard Production -- Scaling -- Points Allocation -- Choosing a Scorecard -- Misclassification -- Scorecard Strength -- Validation -- Notes Chapter 11 Scorecard Development Process, Stage 5: Scorecard Management Reports -- Gains Table -- Characteristic Reports -- Chapter 12 Scorecard Development Process, Stage 6: Scorecard Implementation -- Pre-implementation Validation -- System Stability Report -- Characteristic Analysis Report -- What if the Scorecard Does Not Validate? -- Strategy Development -- General Considerations -- Scoring Strategy -- Setting Cutoffs -- Strategy Development Communication -- Risk-Adjusted Actions -- Policy Rules -- Overrides -- Notes -- Chapter 13 Validating Generic Vendor Scorecards -- Introduction -- Vendor Management Considerations -- Vendor Model Purpose -- Target Population -- Target Definition -- Sample Selection -- Model Estimation Methodology -- Transparency of Vendor Model Estimation -- Factor Selection -- Validation Assessment -- Vendor Model Implementation and Deployment -- Considerations for Ongoing Monitoring -- Examples of Vendor Disclosure Challenges -- Monitoring in Conjunction with Another Score -- Ongoing Quality Assurance of the Vendor -- Get Involved -- Appendix: Key Considerations for Vendor Scorecard Validations -- Notes -- Chapter 14 Scorecard Development Process, Stage 7: Post-implementation -- Scorecard and Portfolio Monitoring Reports -- Credit Application Analysis Reports -- Reacting to Changes -- Portfolio Performance Reports -- Review -- Notes -- Appendix A: Common Variables Used in Credit Scoring -- Appendix B: End-to-End Example of Scorecard Creation -- Bibliography -- About the Author -- About the Contributing Authors -- Index -- EULA Content: Scorecard Development: The People and the Process -- Designing the Infrastructure for Scorecard Development -- Scorecard Development Process, Stage 1: Preliminaries and Planning -- Managing the Risks of In-House Scorecard Development -- Scorecard Development Process, Stage 2: Data Review and Project Parameters -- Default Definition under Basel / Hendrik Wagner -- Scorecard Development Process, Stage 3: Development Database Creation -- Big Data: Emerging Technology for Today's Credit Analyst / Billie Anderson -- Scorecard Development Process, Stage 4: Scorecard Development -- Scorecard Development Process, Stage 5: Scorecard Management Reports -- Scorecard Development Process, Stage 6: Scorecard Implementation -- Validating Generic Vendor Scorecards / Clark Abrahams, Bradley Bender, Charles Maner -- Scorecard Development Process, Stage 7: Post-implementation.