TY - JOUR
T1 - Investigating Customer Churn in Banking: A Machine Learning Approach and Visualization App for Data Science and Management
T2 - A machine learning approach and visualization app for data science and management
AU - Singh, Pahul Preet
AU - Anik, Fahim Islam
AU - Senapati, Rahul
AU - Sinha, Arnav
AU - Sakib, Nazmus
AU - Hossain, Eklas
N1 - Publisher Copyright:
© 2023 Xi'an Jiaotong University
PY - 2024/3/1
Y1 - 2024/3/1
N2 - 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.
AB - 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.
KW - XG boost
KW - bank customer attrition
KW - churn prediction
KW - machine learning
KW - random forest
UR - https://scholarworks.boisestate.edu/electrical_facpubs/563
UR - http://www.scopus.com/inward/record.url?scp=85181666198&partnerID=8YFLogxK
U2 - 10.1016/j.dsm.2023.09.002
DO - 10.1016/j.dsm.2023.09.002
M3 - Article
VL - 7
SP - 7
EP - 16
JO - Electrical and Computer Engineering Faculty Publications and Presentations
JF - Electrical and Computer Engineering Faculty Publications and Presentations
IS - 1
ER -