Advanced Supervised Machine Learning Algorithms in Credit Card Fraud Detection

Simin Yu, Victor Chang, Gia Linh Huỳnh, Vitor Jesus, Jiabin Luo

2025

Abstract

The rapid growth of online transactions has increased convenience but also risks like money laundering, threatening financial systems. Financial institutions use machine learning to detect suspicious activities, but imbalanced datasets challenge algorithm performance. This study uses resampling techniques (SMOTE, ADASYN, Random Undersampling, NearMiss) and ensemble algorithms (XGBoost, CatBoost, Random Forest) on a simulated money laundering dataset provided by IBM (2023) to address this. Our findings reveal that each resampling technique offers unique advantages and trade-offs. CatBoost consistently outperforms XGBoost and Random Forest across sampling techniques, achieving the best balance between precision and recall while maintaining strong ROC curve scores. This strong performance could reduce the number of transactions banks must examine, as investigations would only focus on the predicted laundering cases.

Paper Citation

in Harvard Style

Yu S., Chang V., Huỳnh G., Jesus V. and Luo J. (2025). Advanced Supervised Machine Learning Algorithms in Credit Card Fraud Detection. In Proceedings of the 7th International Conference on Finance, Economics, Management and IT Business - Volume 1: FEMIB; ISBN 978-989-758-748-1, SciTePress, pages 126-138. DOI: 10.5220/0013485400003956

in Bibtex Style

@conference{femib25,
author={Simin Yu and Victor Chang and Gia Huỳnh and Vitor Jesus and Jiabin Luo},
title={Advanced Supervised Machine Learning Algorithms in Credit Card Fraud Detection},
booktitle={Proceedings of the 7th International Conference on Finance, Economics, Management and IT Business - Volume 1: FEMIB},
year={2025},
pages={126-138},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013485400003956},
isbn={978-989-758-748-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Finance, Economics, Management and IT Business - Volume 1: FEMIB
TI - Advanced Supervised Machine Learning Algorithms in Credit Card Fraud Detection
SN - 978-989-758-748-1
AU - Yu S.
AU - Chang V.
AU - Huỳnh G.
AU - Jesus V.
AU - Luo J.
PY - 2025
SP - 126
EP - 138
DO - 10.5220/0013485400003956
PB - SciTePress