Neural networks based on evolutional algorithm for residential loan

Abstract

Residential loan plays an important role for commercial banks to keep away from credit risks. This paper uses neural networks for residential loan, and trains the networks with two evolutional algorithms-genetic algorithm (GA) and particle swarm optimization (PSO). And a GA neural network and a PSO neural network are constructed respectively. The two neural networks are used to classify the residential loan data of commercial banks. Compared with BP neural network, the results indicate that GA network and PSO network give 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 modelpsilas robustness, the accuracy differences between 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 give a better robustness.