Investigating Customer Churn in Banking: A Machine Learning Approach and Visualization App for Data Science and Management: A machine learning approach and visualization app for data science and management

Pahul Preet Singh, Fahim Islam Anik, Rahul Senapati, Arnav Sinha, Nazmus Sakib, Eklas Hossain

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

<p> <p id="x-x-x-abspara0010"> 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&rsquo;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&rsquo;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. </p></p>
Original languageAmerican English
Pages (from-to)7-16
Number of pages10
JournalElectrical and Computer Engineering Faculty Publications and Presentations
Volume7
Issue number1
DOIs
StatePublished - 1 Mar 2024

Keywords

  • XG boost
  • bank customer attrition
  • churn prediction
  • machine learning
  • random forest

EGS Disciplines

  • Electrical and Computer Engineering

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