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Zusatztext It is a superb mixture of a practical how to do guide for those wanting to use and develop credit scoring together with a way of putting the decisions it supports in context and the techniques it uses in a general modelling framework. Klappentext Credit scoring aims to quantify the likelihood of a prospective borrower defaulting on payment over a specified period of time. The credit score is calculated using increasingly sophisticated statistical models, which vary considerably between individual cases. This clearly-written and comprehensive text covers the scorecard development process and provides a practical how-to guide for those wanting to use and develop credit scoring techniques. Assuming little prior knowledge, the text includes the relevant statistical and mathematical tools, numerous real-life examples, and discussion of the credit risk management cycle and the importance of credit scoring in business and regulatory environments, including Basel II. An extensive glossary and bibliography make this an indispensable desktop reference for graduate students in statistics, business, economics and finance, MBA students, credit risk and financial practitioners. Zusammenfassung The Credit Scoring Toolkit provides an all-encompassing view of the use of statistical models to assess retail credit risk and provide automated decisions. In eight modules, the book provides frameworks for both theory and practice. It first explores the economic justification and history of Credit Scoring, risk linkages and decision science, statistical and mathematical tools, the assessment of business enterprises, and regulatory issues ranging from data privacy to Basel II. It then provides a practical how-to-guide for scorecard development, including data collection, scorecard implementation, and use within the credit risk management cycle. Including numerous real-life examples and an extensive glossary and bibliography, the text assumes little prior knowledge making it an indispensable desktop reference for graduate students in statistics, business, economics and finance, MBA students, credit risk and financial practitioners. Inhaltsverzeichnis Preface ; A SETTING THE SCENE ; 1. Credit scoring and the business ; 2. Credit micro-histories ; 3. The mechanics of credit scoring ; B RISKY BUSINESS ; 4. The theory of risk ; 5. Decision science ; 6. Assessing enterprise risk ; C STATS AND MATHS ; 7. Predictive statistics 101 ; 8. Measures of separation/divergence ; 9. Odds and ends ; D DATA! ; 10. Data considerations and design ; 11. Data sources ; 12. Scoring structure ; 13. Information sharing ; 14. Data preparation ; E SCORECARD DEVELOPMENT ; 15. Transformation ; 16. Characteristic selection ; 17. Segmentation ; 18. Reject inference ; 19. Scorecard calibration ; 20. Validation ; 21. Development management issues ; F IMPLEMENTATION AND USE ; 22. Implementation ; 23. Overrides! referrals! and controls ; 24. Monitoring ; 25. Finance ; G RISK MANAGEMENT CYCLE ; 26. Marketing ; 27. Application processing ; 28. Account management ; 29. Collection and recoveries ; 30. Fraud ; H REGULATORY ENVIRONMENT ; 31. Regulatory concepts ; 32. Data privacy and protection ; 33. Anti-discrimination ; 34. Fair lending ; 35. Capital adequacy ; 36. Know your customer (KYC) ; 37. National differences ; Z REFERENCE MATERIALS ; 38. Glossary / Dictionary ; 39. Bibliography ; 40. Appendices ; INDEX ...
