Machine Learning Techniques for Predicting Customer Churn in A Credit Card Company

Victor Chang, Xianghua Gao, Karl Hall, Emmanuel Uchenna

Research output: Chapter in Book/Published conference outputConference publication

Abstract

As marketplaces have become increasingly crowded, businesses have recognized the importance of focusing their business strategy on identifying customers who are likely to leave their services. To solve this, a technique for identifying these consumers must launch pre-emptive retention efforts to keep them. Therefore, to minimize costs and maximize efficiency, churn prediction must be as precise as possible to guarantee retention efforts are directed solely at customers who intend to transfer service providers. The study conducted in this report aims to establish a mechanism for anticipating churn in advance while minimizing misclassification. The suggested methodology integrates a temporal dimension into customer churn prediction to maximize future attrition capture by identifying probable customer loss as soon as possible. Six machine learning algorithms are selected and conducted to validate the suggested methodology using a bank credit card dataset. Finally, the proposed methodology’s results are compared to those published churn prediction methodologies. According to the research, clients can be classified into clusters based on their contracts with the service provider. It is conceivable to estimate when the customer might be expected to end their service with the organization.
Original languageEnglish
Title of host publication 2022 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)
PublisherIEEE
Pages199-207
Number of pages8
ISBN (Electronic)9781665454551
DOIs
Publication statusPublished - 23 Mar 2023
Event2022 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC) - Beijing, China
Duration: 23 Sept 202225 Sept 2022

Publication series

NameProceedings - 2022 International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2022

Conference

Conference2022 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)
Country/TerritoryChina
CityBeijing
Period23/09/2225/09/22

Bibliographical note

Copyright ©2022 IEEE

Keywords

  • credit card churn prediction
  • exploratory data analysis
  • machine learning

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