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A non-parametric procedure to estimate a linear discriminant function with an application to credit scoring

By Raquel Voorduin


The present work studies the application of two group discriminant analysis in the field of\ud credit scoring. The view here given provides a completely different approach to how this\ud problem is usually targeted. Credit scoring is widely used among financial institutions\ud and is performed in a number of ways, depending on a wide range of factors, which\ud include available information, support data bases, and informatic resources. Since each\ud financial institution has its own methods of measuring risk, the ways in which an applicant\ud is evaluated for the concession of credit for a particular product are at least as many\ud as credit concessioners. However, there exist certain standard procedures for different\ud products. For example, in the credit card business, when databases containing applicant\ud information are available, usually credit score cards are constructed. These score cards\ud provide an aid to qualify the applicant and decide if he or she represents a high risk for\ud the institution or, on the contrary, a good investment. Score cards are generally used in conjunction with other criteria, such as the institution's own policies.\ud In building score cards, generally parametric regression based procedures are used,\ud where the assumption of an underlying model generating the data has to be made. Another\ud aspect is that, in general, score cards are built taking into consideration only the\ud probability that a particular applicant will not default.\ud In this thesis, the objective will be to present a method of calculating a risk score that,\ud does not depend on the actual process generating the data and that takes into account\ud the costs and profits related to accepting a particular applicant. The ultimate objective\ud of the financial institution should be to maximise profit and this view is a fundamental\ud part of the procedure presented here

Topics: HG, QA
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