Machine Learning in Banking: Enhancing Marketing Campaigns Through Predictive Analytics

DOI

10.2991/978-94-6239-707-1_13How to use a DOI?

Keywords

Machine Learning (ML); Banking Industry; Customer Segmentation; Targeted Marketing; Fraud Detection

Abstract

Machine Learning (ML) has revolutionized the banking sector by enabling precise customer segmentation, marketing, and fraud analysis. This study entails predicting customer churn from a credit card customer dataset, which is demographic, transactional, and account-based. Supervised Machine Learning algorithms were applied to classify customers according to their churn likelihood. Results highlight the strength of ensemble learning methods like XGBoost and Random Forest in improving classification results. Additionally, this study explores the broader impact of ML on the banking sector, including the optimization of marketing campaigns, enhanced fraud protection, and reduced operational costs. Spending on ML-based banking solutions is substantial, with major banks like JPMorgan Chase integrating AWS AI tools for massive data processing and Commonwealth Bank of Australia allocating nearly $1.1 billion to technology spending. Projections indicate that AI is expected to yield cost savings of up to $1 trillion by 2030 and an estimated profit increase of $340 billion by 2025. These findings provide financial institutions with datadriven insights to improve customer retention and efficiency.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Divyansh Garg
AU  - Kunjal Joshi
AU  - Janhvi Vanga
AU  - Avinash Tandle
PY  - 2026
DA  - 2026/06/18
TI  - Machine Learning in Banking: Enhancing Marketing Campaigns Through Predictive Analytics
BT  - Proceedings of the International Conference on Recent Advances in Intelligent and Sustainable Technologies (RAIST 2026)
PB  - Atlantis Press
SP  - 143
EP  - 153
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-707-1_13
DO  - 10.2991/978-94-6239-707-1_13
ID  - Garg2026
ER  -