Affiliations: [a] School of Economics, Yunnan University of Finance and Economics, Kunming, China
| [b] College of International Economics and Trade, Ningbo University of Finance and Economics, Ningbo, China
| [c] School of Finance, Yunnan University of Finance and Economics, Kunming, China
Abstract: In view of the current demand for risk identification and classification prevention of bank outlets caused by the difficulty in identifying operational efficiency and wind control capability, a risk data measurement and warning classification model based on information entropy and BP neural network is proposed. The model establishes two-level risk data measurement elements from three dimensions. Based on the data set itself, the information entropy is used to determine the weights of the two-level risk elements, and then calculates the risk quantities recorded under the first-level risk measurement elements in the data set. The BP neural network is used to output the risk data classification results without presupposing the weights of the measurement. The proposed model obtains smaller reductions and higher classification accuracies with relatively low computational cost. Experiments show that the model can measure and classify risk data with very low mis-judgment rate and small mis-judgment bias.
Keywords: Information Entropy, BP neural network, risk classification
