• Proceedings of the Korea Association of Information Systems Conference
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• 2005.12a
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• pp.334-346
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• 2005

The purpose of this paper is to introduce the concept of worst practice DEA, which aims at identifying worst performers by placing them on the efficient frontier. This is particularly relevant for our application to credit risk evaluation, but this also has general relevance since the worst performers are where the largest improvement potential can be found. The paper also proposes to use a layering technique instead of the traditional cut-off point approach, since this enables incorporation of risk attitudes and risk-based pricing. Finally, it is shown how the use of a combination of normal and worst practice DEA models enable detection of self-identifiers. The results of the empirical application on credit risk evaluation validate the method which is proposed in this paper.