Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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Identifying Credit Card Fraud Using Cutting-Edge Machine Learning Methods

Authors
Suhail Ahmed1, Aniruddha Das1, *, Izhan Abdullah1, Rajasekar Velswamy1
1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, India
*Corresponding author. Email: ad5096@srmist.edu.in
Corresponding Author
Aniruddha Das
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_58How to use a DOI?
Keywords
Credit Card Fraud Detection; SMOTE; Random Forest; Logistic Regression; Streamlit; Ensemble Learning; Machine Learning
Abstract

With the increasing shift to digital financial systems, detecting credit card fraud has become an ongoing challenge for banks and institutions. This paper introduces a comprehensive machine learning-based fraud detection system that combines oversampling through SMOTE with ensemble classification models. Utilizing the PaySim simulation dataset, the study shows how class imbalance can be tackled to enhance recall while maintaining precision. The system evaluates both Logistic Regression and Random Forest classifiers and deploys the optimal model using a Streamlit-based interface. Unlike traditional models that focus solely on accuracy, our solution emphasizes real-time applicability, interpretability, and ease of use, thus creating a practical bridge between machine learning research and its real-world application in fraud monitoring.

Copyright
© 2025 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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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_58How to use a DOI?
Copyright
© 2025 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.

Cite this article

risenwbib
TY  - CONF
AU  - Suhail Ahmed
AU  - Aniruddha Das
AU  - Izhan Abdullah
AU  - Rajasekar Velswamy
PY  - 2025
DA  - 2025/10/31
TI  - Identifying Credit Card Fraud Using Cutting-Edge Machine Learning Methods
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 704
EP  - 714
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_58
DO  - 10.2991/978-94-6463-866-0_58
ID  - Ahmed2025
ER  -