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