Customer Churn Analysis in Financial Domain using Deep Intelligence Network

Customer churn analysis is regarded as a crucial indicator that determines the revenues and profitability of the organisation in the modern day due to the advancement of technology and business models. Regardless of the company's size including startups, it is relevant to all business sectors, mainly because 65% of the company's revenue comes from its current clients. The aim of the analysis of customer churn is to identify the clients most likely to leave the existing organisation. In order to accurately predict customer churn in the banking industry, this research aims to develop an optimized deep neural network model. Five predictive analytical models from various categories of machine learning algorithms are also compared to the suggested model in the comparison study such as Gaussian Naive Bayes algorithms, decision trees, logistic regression and Artificial neural networks. The outcome demonstrates that, when compared to all other predictive models, the proposed deep intelligence network model exceeds them all in terms of accuracy.