Tremendous growth in the credit industry has spurred the need for Credit Scoring and Its Applications, the only book that details the mathematical models that help creditors make intelligent credit risk decisions.

Creditors of all types make risk decisions every day, often haphazardly. This book addresses the two basic types of decisions and offers sound mathematical models to assist with the decision-making process. The first decision creditors face is whether to grant credit to a new applicant (credit scoring), and the second is how to adjust the credit restrictions or the marketing effort directed at a current customer (behavioral scoring). The authors have filled an important niche with this groundbreaking book. Currently, only the most sophisticated creditors use the models contained in this book to make these decisions, but all creditors can know these aids to successful lending.

The book contains a comprehensive review of the objectives, methods, and practical implementation of credit and behavioral scoring. The authors review principles of the statistical and operations research methods used in building scorecards, as well as the advantages and disadvantages of each approach. The book also contains a description of practical problems encountered in building, using, and monitoring scorecards and examines some of the country-specific problems caused by bankruptcy, equal opportunities, and privacy legislation. This important feature addresses the fact that the credit lending industry has become more international as consumers from one country use credit cards from lending institutions of a second country to make purchases in a third country.

Also included in this book is a discussion of economic theories of consumers' use of credit. The reader will gain an understanding of what lending institutions seek to achieve by using credit scoring and the changes in their objectives. Despite their widespread use in business, no other book details credit scoring variations that should be used with standard statistical and operations research techniques such as discriminant analysis, logistic regression, linear programming, neural nets, and genetic algorithms. Other unique features include methods of monitoring scorecards and deciding when to update them, as well as different applications of scoring, including direct marketing, profit scoring, tax inspection, prisoner release, and payment of fines.

Focusing on small data problems is useful pedagogically; therefore, the authors have included a CD-ROM containing a database, mainly to emphasize the data analysis aspects of credit scoring.

Audience This book is an indispensable reference to credit analysts, scorecard developers, and credit risk managers employed by lending companies such as banks, finance houses, mortgage companies, credit card companies, retailers, mail order firms, utility companies, and insurance companies. Graduate students in mathematical finance, industrial mathematics, and statistics and senior undergraduate students in mathematics, statistics, and quantitative business studies courses will find this a most useful textbook.

Contents Preface; Chapter 1: The History and Philosophy of Credit Scoring; Chapter 2. The Practice of Credit Scoring; Chapter 3: Economic Cycles and Lending and Debt Patterns; Chapter 4: Statistical Methods for Scorecard Development; Chapter 5: Nonstatistical Methods for Scorecard Development; Chapter 6: Markov Chain Models of Repayment and Usage Behavior; Chapter 7: Measuring Scorecard Performance; Chapter 8: Practical Issues of Scorecard Development; Chapter 9: Implementation and Areas of Application; Chapter 10: Applications of Scoring in Other Areas of Lending; Chapter 11: Applications of Scoring in Other Areas; Chapter 12: New Ways to Build Scorecards; Chapter 13: International Differences; Chapter 14: Profit Scoring, Risk-Based Pricing, and Securitization; References; Index.