Neural Network-and-Logit-Based Modeling Strategy for Small Business Credit Scoring (CROSBI ID 494838)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Benšić, Mirta ; Bohaček, Zoran ; Šarlija, Nataša ; Zekić-Sušac, Marijana
engleski
Neural Network-and-Logit-Based Modeling Strategy for Small Business Credit Scoring
Credit scoring has been so far investigated using both logistic regression and neural networks mostly for the purpose of comparing the accuracy of two methods (Desai et al, 1997 ; West, 2000), using commonly recognized credit scoring models. However, due to specific characteristics of small business loans, the importance of selecting different variables from other company loans is emphasized by practitioners and researchers (Feldman, 1997). Specific economic conditions, especially in transitional countries, that also influence model effectiveness, emphasize a close relationship between methodology accuracy and variable selection. This paper investigates such relationship by performing a neural network forward cross-validation modeling strategy based on hit rates, logit univariate and logit forward selection analysis. Comparing the accuracy of both methods, the system is able to extract the best model for the given data. Tested on a Croatian small business loans dataset, it proposes the set of important features for credit scoring in that specific economic environment.
credit scoring; small business loans; neural networks; logistic regression; nonlinear modeling strategy
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Podaci o prilogu
2002.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the Ninth International Conference Forecasting Financial Markets : Advances for Exchange Rates, Interest Rates and Asset Management
Dunis, C. ; Dempster, M.
London : Delhi:
Podaci o skupu
International Conference Forecasting Financial Markets : Advances for Exchange Rates, Interest Rates and Asset Management (9 ; 2002)
predavanje
29.05.2002-31.05.2002
London, Ujedinjeno Kraljevstvo