Credit risk optimization model for crowdfunding process by using Neural Network(MLP)

The purpose of this study is predict and design Credit risk model for debut crowdfunding .According, the complexity of the risk assessment the best neural network architecture with Customize hidden layer neurons selected Multilayer perceptron algorithm for simulation. The statistical population of this study is the financial information of credit / loan file of all customer (506 cases) one of the banks of the country for the year 1997-98. In order to show the significant relationship the extracted indices of the sample and the model output variables (non-default and default), the sample member tested by regression.Thus, thirteen indices entered to the model neural network input vector with three hidden layers in non-default and default groups. In the simulation results, the proposed model was able to optimize the weights of each of the inputs to the network with lower prediction error and 94.1% efficiency .also the average error absolute value obtained for training data (0.88), test data (0.94) and evaluation data (0.84) indicating high capability of the proposed model. According to the research Results, among the indices, income, 0.163 weight, Current Account weight 0.123 are more important, but “degree of education of education” 0.053 are less important in the non-defaulted group.