Classification Algorithms Based On Neural Network and Its Application In the Credit Market

The different types of classification algorithms for credit scoring systems are used to evaluate credit risk. Neural network-based systems that allow the system, through an analysis of historical data, to determine the relationship between account characteristics and the probability of default. The backpropagation algorithm -the multilayer feedforward network structure is described based on data with nine financial ratios from 81 firms listed in china. A simulation on network is made. Neural network model for classification algorithms are established. By varying network parameters we demonstrate that Levenberg Marque training error is smallest among 4 learning algorithms and its performance is better. Increasing the number of hidden layer can result in minor improvement.