Research Article
A Machine Learning Approach to Credit Card Customer Segmentation for Economic Stability
@INPROCEEDINGS{10.4108/eai.27-10-2023.2342007, author={Yujuan Qiu and Jianxiong Wang}, title={A Machine Learning Approach to Credit Card Customer Segmentation for Economic Stability}, proceedings={Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27--29, 2023, Tianjin, China}, publisher={EAI}, proceedings_a={ICEMBDA}, year={2024}, month={1}, keywords={machine learning; customer segmentation; credit card usage; economic stability}, doi={10.4108/eai.27-10-2023.2342007} }- Yujuan Qiu
Jianxiong Wang
Year: 2024
A Machine Learning Approach to Credit Card Customer Segmentation for Economic Stability
ICEMBDA
EAI
DOI: 10.4108/eai.27-10-2023.2342007
Abstract
Credit card usage is a vital component of the global economy, but unpredictable customer behavior poses significant challenges. Machine learning (ML) has emerged as a powerful tool for customer segmentation in the credit card industry. This paper systematically examines different clustering algorithms to identify the most effective approach for accurately categorizing credit card customers. The evaluation of clustering model accuracy is conducted through the Davies-Bouldin Index, Silhouette Score, and Calinski-Harabasz Index. This systematic approach aims to advance our understanding of how ML can be optimally harnessed to enhance customer segmentation, ultimately contributing to economic stability and growth.
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