Predicting customer churn in banking with EKI’s algorithms for adapting Vietnamese market

Abstract

With the rapid advancement of technology, a specific branch of artificial intelligence, known as machine learning, has emerged. This technique has significant potential and plays a crucial role in various industries, including digital transformation, finance, and banking. One of its applications is in identifying potential customers and mitigating risks that may lead to customer attrition. In this article, we will discuss the use of a database, called Churn Modeling, which collects statistical data from banks. We will also explore the application of the BernoulliNB algorithm, combined with the incremental machine learning method, to process streaming data and analyze and predict customer churn rates in banks. The ultimate goal is to provide timely solutions to retain customers. The experimental results demonstrate that this combination yields positive outcomes and has been successfully implemented in the development of a prototype electronic platform.