Neural networks based on evolutional algorithm for personal credit scoring

Abstract

Personal credit scoring plays an important role for commercial banks to keep away from consumer credit risks. This paper used neural networks for personal credit scoring and used two evolutional algorithms of genetic algorithm (GA) and particle swarm optimization (PSO) to train the networks to construct a GA neural network and a PSO neural network respectively. The two neural networks were used to classify the consumer credit data of commercial banks. Compared with BP neural network, the results indicate that GA network and PSO network get lower accuracies on training samples, but on testing samples, the accuracies of GA network and PSO network are higher than that of BP network by 0.38% and 0.76% respectively. On model’s robustness, the accuracy differences on the two groups of samples of GA network and PSO network are lower than that of BP network by 2.08% and 1.33% respectively, which indicate that GA neural network and PSO neural network get a better robustness.