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This paper investigates the credit scoring accuracy of five neural network models: multilayer perceptron, mixture-of-experts, radial basis function, learning vector quantization, and fuzzy adaptive resonance. The neural network credit scoring models are tested using 10-fold crossvalidation with two real world data sets. Results are benchmarked against more traditional methods under consideration for commercial applications including linear discriminant analysis, logistic regression, ii nearest neighbor, kernel density estimation, and decision trees. Results demonstrate that the multilayer perceptron 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.
Keyword:
credit scoring
neural networks
multilayer perceptron
radial basis function
mixture-of-experts
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