Modelling small‐business credit scoring by using logistic regression, neural networks and decision trees

Abstract

Previous research on credit scoring that used statistical and intelligent methods was mostly focused on commercial and consumer lending. The main purpose of this paper is to extract important features for credit scoring in small‐business lending on a dataset with specific transitional economic conditions using a relatively small dataset. To do this, we compare the accuracy of the best models extracted by different methodologies, such as logistic regression, neural networks (NNs), and CART decision trees. Four different NN algorithms are tested, including backpropagation, radial basis function network, probabilistic and learning vector quantization, by using the forward nonlinear variable selection strategy. Although the test of differences in proportion and McNemar's test do not show a statistically significant difference in the models tested, the probabilistic NN model produces the highest hit rate and the lowest type I error. According to the measures of association, the best NN model also shows the highest degree of association with the data, and it yields the lowest total relative cost of misclassification for all scenarios examined. The best model extracts a set of important features for small‐business credit scoring for the observed sample, emphasizing credit programme characteristics, as well as entrepreneur's personal and business characteristics as the most important ones. Copyright © 2005 John Wiley & Sons, Ltd.

Nearest Neighbor Classifier Method for Making Loan Decision in Commercial Bank

“…Each node in the tree specifies a test of some attribute of the instance and each branch descending from that node corresponds to one of the possible values for this attribute [16]. Advantages of using decision learning tree algorithms are: 1) They generalize in a better way for unobserved instances, once examined the attribute value pair in the training data.…”

Assessment of Financial Risk Prediction Models with Multi-criteria Decision Making Methods

“…Baesens et al [3] showed that the support vector machines achieve the highest accuracy rate, while the neural networks perform the best in terms of the area under the ROC curve. Bensic et al [4] suggested that the accuracy of probabilistic neural network is superior to that of logistic regression, CART decision trees, radial basis function, multi-layer perceptron and learning vector quantization. Antonakis and Sfakianakis [2] evaluated the performance of k-nearest neighbors decision rule, multi-layer perceptron, decision trees, logistic regression, linear discriminant analysis and naïve Bayes, showing that the k-nearest neighbors rule achieved the highest accuracy and the neural network was the best method in terms of the Gini coefficient.…”

An entropy-LVQ system for S&P500 downward shifts forecasting

“…The performance of the LVQ will be compared to the performance of the probabilistic neural network (PNN) due to its effectiveness in financial classification problems (Schierholt & Dagli, 1996;Prokhorov & Wunsch, 1995, 1998Bensic et al, 2005;Mehrara et al, 2010). However, to the best of our knowledge the LVQ neural network has not been used for stock market modelling and forecasting.…”