Mining and Analysis of Power Data Models Based on Neural Network Prediction

Abstract

In recent years, with the transformation of our country’s economic structure, the consumption level of residents has gradually increased, and the volume of various consumer credit businesses of commercial banks has increased sharply, which has made the bank’s risk management and control work facing huge challenges. Traditional risk assessment methods mainly rely on the market experience of credit personnel. The results of risk assessment are greatly affected by personal subjective factors. In addition, in the face of increasing data volume and business volume, traditional assessment methods are inefficient, have long business cycles, and are accurate. It is difficult to guarantee. Therefore, this article aims to study the application of electricity data in personal credit risk assessment of commercial banks. On the basis of analyzing the causes of personal credit risk, the characteristics of personal credit risk and the application of electricity data in personal credit risk assessment, a personal credit assessment model is established through the data mining method of neural network, and the model is predicted. The prediction results show that the overall prediction accuracy rate of the model on the training data set is 86%, and the prediction accuracy rate of the test data set is 78%. The neural network model has high prediction accuracy, low data requirements and good results.