Investigating Customer Churn in Banking - A Machine Learning Approach and Visualization App for Data Science and Management

Aug 6, 2026

Upload by Anda

Customer attrition in banking occurs when clients stop using a bank’s products and services and later end their relationship, making retention critical in a highly competitive market. The study examines banking data to predict which users are likely to churn and cease becoming paying customers. Multiple machine learning models are used for comparative analysis across evaluation metrics, and a Data Visualization RShiny app is developed to support churn analysis. Insights help identify attrition trends so banks can retain at-risk customers.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Customer attrition and its impact in banking

Predictive modeling with machine learning

Visualization support via RShiny app

Boise State University ScholarWorks | Electrical and Computer Engineering Faculty Publications and Presentations | Department of Electrical and Computer Engineering | | --- | --- | 3-2024 Investigating Customer Churn in Banking: A Machine Learning Approach and Visualization App for Data Science and Management Pahul Preet Singh State University of New York, University at Buffalo Fahim Islam Anik Khulna University of Engineering & Technology Rahul Senapati State University of New York, University at Buffalo Arnav Sinha State University of New York, University at Buffalo Nazmus Sakib Kennesaw State University See next page for additional authors Authors Pahul Preet Singh, Fahim Islam Anik, Rahul Senapati, Arnav Sinha, Nazmus Sakib, and Eklas Hossain This article is available at ScholarWorks: [https://scholarworks.boisestate.edu/electrical_facpubs/563](https://scholarworks.boisestate.edu/electrical_facpubs/563) Data Science and Management 7 (2024) 7–16 Contents lists available at ScienceDirect Data Science and Management journal [homepage:](homepage: www.keaipublishing.com/en/journals/data-science-and-management)[ www.keaipublishing.com/en/journals/data-science-and-management](homepage: www.keaipublishing.com/en/journals/data-science-and-management) Research article Investigating customer churn in banking: A machine learning approach and visualization app for data science and management Pahul Preet Singh a, Fahim Islam Anikb, Rahul Senapati a, Arnav Sinha a, Nazmus Sakib c, *, Eklas Hossain d a Institute for Artiﬁcial Intelligence and Data Science, University at Buffalo, The State University of New York, United States b Department of Mechanical Engineering, Khulna University of Engineering & Technology, Bangladesh c Department of Information Technology, Kennesaw State University, United States d Department of Electrical and Computer Engineering, Boise State University, United States | A R T I C L E I N F O | | A B S T R A C T | | --- | --- | --- | | Keywords:
Bank customer attrition Churn prediction Machine learning
XG boost
Random forest | | 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. | 1. Introduction Customer attrition, also known as customer churn, is the phenomenon where customers terminate their relationship with a business or organization. In the context of banking, customer attrition occurs when customers close their accounts or discontinue utilizing services of a particular bank. Effectively understanding and managing customer attrition are crucial for banks to maintain ﬁnancial stability and safeguard their reputation. The ﬁnancial impact of customer attrition on banks can be signiﬁcant, resulting in potential revenue loss across various banking services. Consequently, establishing and nurturing long-term customer relationships is highly valuable for banks. By gaining insight into attrition patterns, banks can identify customers at risk of leaving and implement strategies to retain them. This

FAQs

What problem does the research address in the banking context?

The research addresses customer attrition (churn) in banking, where clients discontinue using a bank’s services and eventually end their relationship.

How is customer churn predicted in the proposed approach?

The study analyzes bank data using multiple machine learning algorithms and compares them using different evaluation metrics.

What tool is provided to support churn analysis beyond modeling?

A Data Visualization RShiny app is developed to help users analyze customer churn and interpret results for data science and management use.

Aug 6, 2026

Upload by Anda