The consumer loan default predicting model - An application of DEA-DA and neural network
In this paper we construct the consumer loan default predicting model through conducting the empirical analysis on the customers of unsecured consumer loan from a certain financial institution in Taiwan, and adopt the borrower's demographic variables and money attitude as the real-timeaneous discriminant information. Furthermore, we construct respectively through four predicting methods, such as DA, LR, NN and DEA-DA, to compare the suitability of these four mentioned methods. The results show that DEA-DA and NN are possessed better predicting capability and they are the optimal predicting model that this study longing for. In addition, this study showed that the default loan predicting model will be possessed higher level of predicting capability after added money attitude. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved.
consumer loans dea-da neural networks money attitude dea logistic regression