Deep Learning based Predictive Analytics for Employees Turnover with Performance Prediction in Banking Sector

The banking sector is a dynamic and competitive environment. Predictive analytics is necessary to monitor employee turnover and predict performance. This is initiated using the proposed system which involves deep learning techniques. The Convolutional Neural Network helps in extracting the information regarding the diverse data which involves employee performance, historical turnover and satisfaction factors. The convolutional layers help in automatically identifying the necessary information from the data. The sequential and time-dependent parameters of the employee turnover prediction are done using a Recurrent Neural Network. The banking sector provides temporal workforce data where RNN can able to capture patterns in the dynamic nature of employee performance and identification of potential turnover indicators. The gradient problem in the system is solved using Long Short-Term Memory networks. Employee behaviour and performance are the early indicators of potential turnover in banking sectors. The proposed system helps to forecast employee performance and turnover based on trend analysis. The various factors that influence turnover and performance are individual performance metrics, job satisfaction and market trends. The proposed system has the ability to adapt to the dynamic environment while providing continuous improvement.