Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors
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 use...
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| Format: | Article |
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Sciendo
2017-12-01
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| Series: | Folia Oeconomica Stetinensia |
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| Online Access: | https://doi.org/10.1515/foli-2017-0023 |
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doaj-9fedf8d34abd4b9f8b18ec3b9ad180b82021-09-05T20:45:02ZengSciendoFolia Oeconomica Stetinensia1898-01982017-12-0117212914310.1515/foli-2017-0023foli-2017-0023Capacity of Neural Networks and Discriminant Analysis in Classifying Potential DebtorsPiasecki Krzysztof0Wójcicka-Wójtowicz Aleksandra1Poznań University of Economics and Business, Department of Investment and Real Estate, Niepodległości 10, 61-875Poznań, PolandPoznań University of Economics and Business, Department of Operations Research, Niepodległości 10, 61-875Poznań, PolandIdentifying 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.https://doi.org/10.1515/foli-2017-0023credit riskdefaultneural networksdiscriminant analysisfinancial indicesg33c38c49 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Piasecki Krzysztof Wójcicka-Wójtowicz Aleksandra |
| spellingShingle |
Piasecki Krzysztof Wójcicka-Wójtowicz Aleksandra Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors Folia Oeconomica Stetinensia credit risk default neural networks discriminant analysis financial indices g33 c38 c49 |
| author_facet |
Piasecki Krzysztof Wójcicka-Wójtowicz Aleksandra |
| author_sort |
Piasecki Krzysztof |
| title |
Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors |
| title_short |
Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors |
| title_full |
Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors |
| title_fullStr |
Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors |
| title_full_unstemmed |
Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors |
| title_sort |
capacity of neural networks and discriminant analysis in classifying potential debtors |
| publisher |
Sciendo |
| series |
Folia Oeconomica Stetinensia |
| issn |
1898-0198 |
| publishDate |
2017-12-01 |
| description |
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. |
| topic |
credit risk default neural networks discriminant analysis financial indices g33 c38 c49 |
| url |
https://doi.org/10.1515/foli-2017-0023 |
| work_keys_str_mv |
AT piaseckikrzysztof capacityofneuralnetworksanddiscriminantanalysisinclassifyingpotentialdebtors AT wojcickawojtowiczaleksandra capacityofneuralnetworksanddiscriminantanalysisinclassifyingpotentialdebtors |
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1717784664233476096 |