Research of Machine Learning Algorithm in Early Warning Analysis of Bank Customer Churn

With the development of big data and artificial intelligence, it has become an important research topic on how banks use user data to identify the behavior characteristics behind it, and then how to effectively provide early warning for bank customer churn. In order to better deal with the problem of customer churn, and reduce the phenomenon of bank customer churn, the application of machine learning algorithm in bank customer churn early warning analysis is proposed. Combined with the causes of bank customer churn, machine learning algorithms such as decision tree, Bayesian algorithm and random forest algorithm are adopted to carry out early warning analysis of bank customer churn. Through experimental analysis and verification, the random forest algorithm has the best accuracy performance, and the comprehensive accuracy rate has reached more than 76%. However, in terms of algorithm stability, the stability of decision tree algorithm is higher than the other two algorithms. Banks can select appropriate algorithms for early warning analysis according to different characteristics of customers.