Models and algorithms for managing the emotional state of customers in commercial banks using deep convolutional neural networks

Abstract

In this research, a model for managing the emotional state of customers in a commercial bank has been developed using a deep convolutional neural network (DCNN) and algorithms for distributing conflicting customers along the routes to the certain operator, depending on this emotional state. In order to route a customer to the certain operator, it was necessary to develop a mathematical model of emotional target routing based on the Newton interpolation polynomial. The developed model has four classes [angry, happy, neutral, and sad], trained and tested on the well-known FER2013 dataset using machine learning and computer vision. Finally, the model validation accuracy of 70.35% has been achieved.