Skip to Main Content
Constructing a scientific and effective bank risk forewarning model is an important measure to effectively guard against and defuse risks in commercial banks. This article constructs a bank risk forewarning model using BP neural network and principal component analysis method. Meanwhile, being aimed at that it takes a long time while processing the mass data to train the network, it also decomposes the algorithm for MapReduce, running in parallel to reduce the running time. The experiment result shows that the neural network model achieved higher accuracy of rate 88percents.