Credit scoring: Discussion of methods and a case study

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Credit scoring: Discussion of methods and a case study

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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


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