Investigating customer churn in banking: a machine learning approach and visualization app for data science and management

• Published in: Data science and management Vol. 7; no. 1; pp. 7 - 16
• Main Authors: Singh, Pahul Preet, Anik, Fahim Islam, Senapati, Rahul, Sinha, Arnav, Sakib, Nazmus, Hossain, Eklas
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2024; KeAi Communications Co. Ltd
• Subjects: Bank customer attrition; Churn prediction; Machine learning; Random forest; XGboost; Bank customer attrition; XGboost; Random forest; Churn prediction; Machine learning
• ISSN: 2666-7649; 2666-7649
• DOI: 10.1016/j.dsm.2023.09.002

Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and, after that, end their connection with the bank. Therefore, customer retention is essential in today’s extremely competitive banking market. Additionally, having a solid customer base helps attract new consumers by fostering confidence and a referral from a current clientele. These factors make reducing client attrition a crucial step that banks must pursue. In our research, we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying customers. We use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation metrics. In addition, we developed a Data Visualization RShiny app for data science and management regarding customer churn analysis. Analyzing this data will help the bank indicate the trend and then try to retain customers on the verge of attrition.