  We propose a two-stage hybrid approach with neural networks as the new
feature construction algorithms for bankcard response classifications. The
hybrid model uses a very simple neural network structure as the new feature
construction tool in the first stage, then the newly created features are used
as the additional input variables in logistic regression in the second stage.
The model is compared with the traditional one-stage model in credit customer
response classification. It is observed that the proposed two-stage model
outperforms the one-stage model in terms of accuracy, the area under ROC curve,
and KS statistic. By creating new features with the neural network technique,
the underlying nonlinear relationships between variables are identified.
Furthermore, by using a very simple neural network structure, the model could
overcome the drawbacks of neural networks in terms of its long training time,
complex topology, and limited interpretability.
