<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Identifying potential healthy and unsound customers is an important task. The reduction of loans granted to companies of questionable credibility can influence banks’ performance. A prior identification of factors that affect the condition of companies is a vital element. Among the most commonly used methods we can enumerate discriminant analysis (DA), scoring methods, neural networks (NN), etc. This paper investigates the use of different structure NN and DA in the process of the classification of banks’ potential clients. The results of those different methods are juxtaposed and their performance compared.</p></abstract>

Identifying potential healthy and unsound customers is an important task. The reduction of loans granted to companies of questionable credibility can influence banks’ performance. A prior identification of factors that affect the condition of companies is a vital element. Among the most commonly used methods we can enumerate discriminant analysis (DA), scoring methods, neural networks (NN), etc. This paper investigates the use of different structure NN and DA in the process of the classification of banks’ potential clients. The results of those different methods are juxtaposed and their performance compared.

Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors

Poznań University of Economics and Business

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Identifying potential healthy and unsound customers is an important task. The reduction of loans granted to companies of questionable credibility can influence...

Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors

Published Online: Dec 27, 2017

Page range: 129 - 143

Received: Jun 14, 2017

Accepted: Nov 02, 2017

DOI: https://doi.org/10.1515/foli-2017-0023

Keywords
credit risk, default, neural networks, discriminant analysis, financial indices

© 2017 Krzysztof Piasecki et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors

Published Online: Dec 27, 2017

Page range: 129 - 143

Received: Jun 14, 2017

Accepted: Nov 02, 2017

DOI: https://doi.org/10.1515/foli-2017-0023

Keywordscredit risk, default, neural networks, discriminant analysis, financial indices

© 2017 Krzysztof Piasecki et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
credit risk, default, neural networks, discriminant analysis, financial indices