Neural network credit scoring models

This paper investigates the credit scoring accuracy of five neural network models: multilayer perceptron, mixture-of-experts, radial basis function, l earning vector quantization, and fuzzy adaptive resonance. The neural netwo rk credit scoring models are tested using 10-fold crossvalidation with two real world data sets. Results are benchmarked against more traditional meth ods under consideration for commercial applications including linear discri minant analysis, logistic regression, ii nearest neighbor, kernel density e stimation, and decision trees. Results demonstrate that the multilayer perc eptron may not be the most accurate neural network model, and that both the mixture-of-experts and radial basis function neural network models should be considered for credit scoring applications. Logistic regression is found to be the most accurate of the traditional methods.