Credit scoring: Discussion of methods and a case study

Show simple item record

dc.contributor.author	Kronborg, Dorte
dc.contributor.author	Tjur, Tue
dc.date.accessioned	2010-07-02
dc.date.accessioned	2010-07-02T11:23:08Z
dc.date.available	2010-07-02T11:23:08Z
dc.date.issued	2010-07-02
dc.identifier.uri	http://hdl.handle.net/10398/8131
dc.description.abstract	The scenario considered is that of a credit association, a bank or an- other nancial institution which, on the basis of information about a new potential customer and historical data on many other customers, has to decide whether or not to give that customer a certain loan. We discuss three popular techniques: logistic regression, discriminant analysis and neural networks. We shall argue strongly in favour of the logistic regression. Discriminant analysis can be used, and for reasons that can be explained mathematically it will often result in approximately the same conclusions as a logistic regression. But the statistical assumptions are not appropriate in most cases, and the results given are not as directly interpretable as those of logistic re- gression. Neural network techniques, in their simplest form, su er from the lack of statistical standard methods for veri cation of the model and tests for removal of covariates. This problem disappears to some extend when the neural networks are reformulated as proper statistical models, based on the type of functions that are considered in neural networks. But this results in a somewhat specialized class of non{linear regression models, which may be useful in situations where local peculiarities of the response function are in focus, but certainly not when the overall \| usually monotone \| e ect of many more or less confounded covariates is the issue. We discuss, within the logistic regression framework, the handling of phenomena such as time trends and corruption of the historical data due to shifts of policy, censor- ing and/or interventions in highrisk customers' economy. Finally, we illustrate and support the theoretical considerations by a case study concerning mortgage loans in a Danish credit associatio	en_US
dc.format.extent	32 s.	en_US
dc.language	eng	en_US
dc.publisher	Copenhagen Business School	en_US
dc.subject.other	Credit scoring, discriminant analysis, logistic regression, neural	en_US
dc.title	Credit scoring: Discussion of methods and a case study	en_US
dc.type	wp	en_US
dc.accessionstatus	modt10jul02 siso	en_US
dc.contributor.corporation	Copenhagen Business School. CBS	en_US
dc.contributor.department	Center for Statistik	en_US
dc.contributor.departmentshort	CST	en_US
dc.contributor.departmentuk	Center for Statistics	en_US
dc.contributor.departmentukshort	CST	en_US
dc.idnumber	x644964528	en_US
dc.publisher.city	Frederiksberg	en_US
dc.publisher.year	1999	en_US