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Credit Scoring and Its ApplicationsJanuary 2002
Publisher:
  • Society for Industrial and Applied Mathematics
  • 3600 University City Science Center Philadelphia, PA
  • United States
ISBN:978-0-89871-483-8
Published:01 January 2002
Pages:
248
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Abstract

From the Publisher: About the Author Lyn C. Thomas is a Professor of Management Science at the University of Southampton. Jonathan N. Crook is Reader in Business Economics at the University of Edinburgh. David B. Edelman is Credit Director of Royal Bank of Scotland, Edinburgh.

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  2. Feng X, Xiao Z, Zhong B, Dong Y and Qiu J (2019). Dynamic weighted ensemble classification for credit scoring using Markov Chain, Applied Intelligence, 49:2, (555-568), Online publication date: 1-Feb-2019.
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  4. Garrido F, Verbeke W and Bravo C (2018). A Robust profit measure for binary classification model evaluation, Expert Systems with Applications: An International Journal, 92:C, (154-160), Online publication date: 1-Feb-2018.
  5. Tang Y, Ji J, Gao S, Dai H, Yu Y, Todo Y and Aricò P (2018). A Pruning Neural Network Model in Credit Classification Analysis, Computational Intelligence and Neuroscience, 2018, Online publication date: 1-Jan-2018.
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  9. Serrano-Cinca C and Gutiérrez-Nieto B (2016). The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending, Decision Support Systems, 89:C, (113-122), Online publication date: 1-Sep-2016.
  10. Teng L, Zhang W, Tang F, Teng S and Fu X The Study and Application on Multi-dimension and Multi-layer Credit Scoring Revised Selected Papers of the Second International Conference on Human Centered Computing - Volume 9567, (386-399)
  11. Cleofas-Sánchez L, García V, Marqués A and Sánchez J (2016). Financial distress prediction using the hybrid associative memory with translation, Applied Soft Computing, 44:C, (144-152), Online publication date: 1-Jul-2016.
  12. Abdou H, Tsafack M, Ntim C and Baker R (2016). Predicting creditworthiness in retail banking with limited scoring data, Knowledge-Based Systems, 103:C, (89-103), Online publication date: 1-Jul-2016.
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  14. Kozeny V (2015). Genetic algorithms for credit scoring, Expert Systems with Applications: An International Journal, 42:6, (2998-3004), Online publication date: 15-Apr-2015.
  15. Zhao Z, Xu S, Kang B, Kabir M, Liu Y and Wasinger R (2015). Investigation and improvement of multi-layer perceptron neural networks for credit scoring, Expert Systems with Applications: An International Journal, 42:7, (3508-3516), Online publication date: 1-May-2015.
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  24. Bijak K and Thomas L (2012). Does segmentation always improve model performance in credit scoring?, Expert Systems with Applications: An International Journal, 39:3, (2433-2442), Online publication date: 1-Feb-2012.
  25. Louzada F, Ferreira-Silva P and Diniz C (2012). On the impact of disproportional samples in credit scoring models, Expert Systems with Applications: An International Journal, 39:9, (8071-8078), Online publication date: 1-Jul-2012.
  26. Mircea G, Pirtea M, Neamţu M and Băzăvan S Discriminant analysis in a credit scoring model Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications, (257-262)
  27. Lima E, Mues C and Baesens B (2011). Monitoring and backtesting churn models, Expert Systems with Applications: An International Journal, 38:1, (975-982), Online publication date: 1-Jan-2011.
  28. Zhou L, Lai K and Yu L (2010). Least squares support vector machines ensemble models for credit scoring, Expert Systems with Applications: An International Journal, 37:1, (127-133), Online publication date: 1-Jan-2010.
  29. Yu L, Yue W, Wang S and Lai K (2010). Support vector machine based multiagent ensemble learning for credit risk evaluation, Expert Systems with Applications: An International Journal, 37:2, (1351-1360), Online publication date: 1-Mar-2010.
  30. Hand D (2009). Measuring classifier performance, Machine Language, 77:1, (103-123), Online publication date: 1-Oct-2009.
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  32. Xu X, Zhou C and Wang Z (2009). Credit scoring algorithm based on link analysis ranking with support vector machine, Expert Systems with Applications: An International Journal, 36:2, (2625-2632), Online publication date: 1-Mar-2009.
  33. Martens D, Bruynseels L, Baesens B, Willekens M and Vanthienen J (2008). Predicting going concern opinion with data mining, Decision Support Systems, 45:4, (765-777), Online publication date: 1-Nov-2008.
  34. Abdou H, Pointon J and El-Masry A (2008). Neural nets versus conventional techniques in credit scoring in Egyptian banking, Expert Systems with Applications: An International Journal, 35:3, (1275-1292), Online publication date: 1-Oct-2008.
  35. Zhou Y and Elhag T Apply logit analysis in bankruptcy prediction Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization, (302-308)
  36. Lai K, Yu L, Zhou L and Wang S Credit risk evaluation with least square support vector machine Proceedings of the First international conference on Rough Sets and Knowledge Technology, (490-495)
  37. Van Gestel T, Baesens B, Van Dijcke P, Garcia J, Suykens J and Vanthienen J (2006). A process model to develop an internal rating system, Decision Support Systems, 42:2, (1131-1151), Online publication date: 1-Nov-2006.
  38. Li J, Xu W and Shi Y Credit scoring via PCALWM Proceedings of the 5th international conference on Computational Science - Volume Part III, (531-538)
  39. Hand D, Sohn S and Kim Y (2005). Optimal bipartite scorecards, Expert Systems with Applications: An International Journal, 29:3, (684-690), Online publication date: 1-Oct-2005.
Contributors
  • University of Southampton
  • The University of Edinburgh
  • University College Dublin

Recommendations

Reviews

James Speybroeck

Put simply, credit scoring is a set of methods for aiding lenders in the crucial decision of who should and should not receive credit. In this exciting book, the authors present their findings and methods with meticulous detail. The book begins with an excellent and very readable chapter on the history and philosophy behind credit scoring. Chapter 2, on the practice of credit scoring, is a template of the preliminary steps necessary before additional issues are discussed. Chapter 3 is an interesting discussion of economic cycles, and of how those cycles affect the credit scoring procedures. It is in this chapter that the reader is convinced of the authors’ seriousness in presenting their rationale for credit scoring. Their discussion is not cosmetic, and they are very detailed in their presentation. Chapter 4 is a discussion of the statistical methods used in credit scoring. The emphasis is on the use of discriminant analysis and logistic regression. Again, the chapter is very well detailed. Chapter 5 discusses non-statistical methods for credit scoring. Chapter 6 addresses the problems of repayment and usage behavior. Again, the treatment is based on solid mathematical models, and is neither anecdotal nor cosmetic. Chapter 7 addresses assessment of performance in the building of a credit scorecard. Topics include a discussion of error rates, cross-validation, bootstrapping and jackknifing, and separation measures. The delta approach, for comparing actual and predicted performance, is then covered. Chapter 8 provides an overview of some of the practical issues of credit scoring. These issues include sample selection and the choosing of attributes. Chapter 9 discusses the implementation of the credit scorecard. Chapter 10 continues with application, but looks at additional areas of application, such as prescreening, preapproval, fraud prevention, mortgage scoring, small-business scoring, risk pricing, credit extension, transaction authorization, debt recovery, and provisions for bad debt. Chapter 11 discusses credit scoring in other areas of the business community, such as direct marketing, profit scoring, tax inspection, payment of fines, and other less used arenas. Chapter 12 discusses new methods under study for developing effective credit scorecards. Chapter 13 is particularly interesting, comparing and contrasting how different international communities deal with credit scoring. The book concludes with a chapter on the use of credit scoring for a profit-scoring system, rather than a default-risk system. Those involved in credit scoring will not only find this book interesting, they may also find that they have in their possession a paradigm for the industry. The book is exceptionally well documented and detailed. The authors are not only scholars, but also effective writers and practitioners. If you are involved in credit scoring, this text should be in your library. Online Computing Reviews Service

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