Open Access
Issue
SHS Web Conf.
Volume 218, 2025
2025 2nd International Conference on Development of Digital Economy (ICDDE 2025)
Article Number 02012
Number of page(s) 6
Section Finance Tech Advances: Impacts and Innovations
DOI https://doi.org/10.1051/shsconf/202521802012
Published online 03 July 2025
  1. Owolabi, P.C. Uche, N.T. Adeniken, O. Efijemue, S. Attakorah, O.G. Emi-Johnson, E. Hinneh, Comparative analysis of machine learning models for customer churn prediction in the U.S. banking and financial services: Economic impact and industry- specific insights. J. Data Anal. Inf. Process. 12(3), 388–418 (2024) [Google Scholar]
  2. P. Joga, B. Harshini, R. Sahay, Comparative analysis of machine learning models for customer segmentation. In: SpringerLink (1970) [Google Scholar]
  3. A. Amin, J.M. Chatterjee, Comparative analysis of machine learning models for customer segmentation. In: M.K. Shukla, A.K. Misra, A.L. Jameel, S. Gupta (eds.) Advances in Computational Intelligence and Learning, pp. 63–75. Springer (2023) [Google Scholar]
  4. S. Jain, S. Gupta, A review on customer segmentation methods using machine learning. In: Proceedings of the 2021 International Conference on Data Science and Engineering, pp. 333–344. Springer (2021) [Google Scholar]
  5. H. Smolic, How to use machine learning for customer segmentation. Medium (2024). https://hrvoje-smolic.medium.com/how-to-use-machine-learning-for-customer-segmentation-49612667301d [Google Scholar]
  6. A novel approach for customer segmentation and product recommendation to boost sales using machine learning. IEEE Conf. Publ., IEEE Xplore (n.d.). [Google Scholar]
  7. G. Wang, Customer segmentation in the digital marketing using a Q-learning based differential evolution algorithm integrated with K-means clustering. PLoS ONE 20(2) (2025) [Google Scholar]
  8. R. Johnson, T. Smith, K. Brown, Customer segmentation in digital banking using machine learning. Int. J. Bank. Finance 12(2), 78–95 (2021) [Google Scholar]
  9. Y. Chen, L. Zhang, H. Wang, Machine learning for fraud detection in banking: A comprehensive review. J. Financ. Technol. 15(3), 45–60 (2022) [Google Scholar]
  10. S. Lee, J. Kim, H. Park, Personalized treatment plans for diabetes patients using clustering algorithms. Healthc. Inform. Res. 27(4), 210–225 (2021) [Google Scholar]

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