Research on bank customer churn model based on attention network

In recent years, with the popularization of mobile devices and the rapid development of the mobile Internet, the growth of data has led to the obvious development of emerging industries such as Internet finance. In this context, the traditional financial industry has been severely impacted by the Internet finance industry. However, the growth of data is a "double-edged sword", which brings more management costs to enterprises and also contains more valuable information. User churn prediction is one of the most discussed issues in banking business. Exploring a user churn prediction model suitable for the existing data environment is of great significance to the development of banking business. In this paper, the attention weight is added to the three neural networks of LSTM and GRU after the data processing, and more accurate prediction results are obtained.