Small Business Credit Scoring: A Comparison of Logistic Regression, Neural Network and Decision Tree Models
Zekić-Sušac, Marijana; Šarlija, Nataša; Benšić, Mirta;
Small Business Credit Scoring: A Comparison of Logistic Regression, Neural Network and Decision Tree Models;
Proceedings of the 26th International Conference on Information Technology Interfaces, June 7-10., 2004, Cavtat/Dubrovnik, Croatia, pp.265-270
(IEEE Catalog Number 04EX794; ISBN 953-96769-9-1; ISSN 1330-1012)
The paper compares the models for small business credit scoring developed by logistic regression, neural networks, and CART decision trees on a Croatian bank dataset.
The models obtained by all three methodologies were estimated; then validated on the same hold-out sample, and their performance is compared.
There is an evident significant difference among the best neural network model, decision tree model, and logistic regression model.
The most successful neural network model was obtained by the probabilistic algorithm.
The best model extracted the most important features for small business credit scoring from the observed data.