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Intelligent Credit Scoring : Building and Implementing Better Credit Risk Scorecards.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Somerset :
John Wiley & Sons, Incorporated,
2016.
|
| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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: OverviewNotes; 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) SegmentationStatistically 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 ConstructionRandom 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.