An insurance sales prediction model based on deep learning

Abstract

The analysis and prediction of insurance sales enable insurance companies to construct effective and efficient marketing models, conduct targeted promotion campaigns, and expand the range of target consumers, thereby increasing the direct and indirect profits. This paper improves the long short-term memory (LSTM) network into an effective prediction model for insurance sales. Firstly, an original database was created on the historical data of insurance sales, followed by the identification of the linear and nonlinear factors affecting insurance sales. Next, the redundant data were removed from data sequences through nondimensionalization and gray correlation calculation. Finally, multiple linear regression (MLR) was carried out based on LINEST function, completing the prediction of insurance sales. Simulation results show that the improved LSTM network outperformed the other prediction models, indicating that the prediction effect can be improved by data processing through multiple residual predictions.