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Discrete Choice Model Application to the Credit Risk Evaluation

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Abstract

The aim of the paper is to discuss the application of classification functions and artificial neural networks (such as multilayer perceptron and radial basis function) to recognize the risk category of investigated companies. The research is based on data from 295 enterprises that applied for credit in two regional banks operating in Poland. Each firm is described by 13 diagnostic variables and potential borrowers are classified into four classes. The efficiency of classification is evaluated in terms of classification errors calculated from the actual classification made by the credit officers. The results of the experiments show that application of artificial neural networks and classification functions can support the creditworthiness evaluation of borrowers.

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Correspondence to Dorota Witkowska.

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Witkowska, D. Discrete Choice Model Application to the Credit Risk Evaluation. Int Adv Econ Res 12, 33–42 (2006). https://doi.org/10.1007/s11294-006-6124-0

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JEL

  • C10
  • C45