Neural network credit-risk evaluation model based on back-propagation algorithm

Abstract

The research establishes a neural network credit-risk evaluation model by using back-propagation algorithm. The model is evaluated by the credits for 120 applicants. The 120 data are separated in three groups: a "good credit" group, a "middle credit" group and a "bad credit" group. The simulation shows that the neural network credit-risk evaluation model has higher classification accuracy compared with the traditional parameter statistical approach, that is linear discriminant analysis. We still give a learning algorithm and a corresponding algorithm of the model.

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