Using Machine Learning Algorithms to Analyze Customer Churn with Commissions Rate for Stocks in Brokerage Firms and Banks

Abstract

Stock commission rates of banks and brokerage firms are a critical factor for investors. These rates affect the cost of stock investments. It's crucial to highlight the significance of stock commission rates in brokerage firms and banks, as well as the factors that influence their determination. This article aims to draw attention to the study's focus on customer churn and commission rates within the financial industry. Previous research has mainly focused on identifying the key variables affecting customer churn without considering its impact on forecast accuracy. This work has two primary research goals: first one is to investigate how commission rates affect the accuracy of customer churn prediction in brokerage firms and the banking sector using machine learning models, and second one is to compare and evaluate the most effective machine learning approaches for predicting customer churn. The customer churn management approach was enhanced through the analysis of a data set obtained from a bank and brokerage firm. This data set, comprised of 7816 entries and 14 columns, reflects the firm's transactions and was sourced from a publicly accessible database. The analysis employed Decision Tree, Random Forest, K-NN, Gaussian NB, and XGBoost algorithms to evaluate performance using three accuracy measures. Two approaches are included for model creation. According to the first analysis results, the Gaussian NB, for second approach the K-NN algorithms gave the best result.