Naslov
Neural Network-and-Logit-Based Modeling Strategy for Small Business Credit Scoring

Autori
Benšić, Mirta ; Bohaček, Zoran ; Šarlija, Nataša ; Zekić-Sušac, Marijana

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the Ninth International Conference Forecasting Financial Markets : Advances for Exchange Rates, Interest Rates and Asset Management / Dunis, C. ; Dempster, M. - London : Delhi, 2002

Skup
International Conference Forecasting Financial Markets : Advances for Exchange Rates, Interest Rates and Asset Management (9 ; 2002)

Mjesto i datum
London, Ujedinjeno Kraljevstvo, 29.05.2002. - 31.05.2002

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
credit scoring; small business loans; neural networks; logistic regression; nonlinear modeling strategy

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Ekonomija

POVEZANOST RADA