Enhanced Credit Card Fraud Detection Using Federated Learning, LSTM Models, and the SMOTE Technique

Weddou Mohamedhen, Maha Charfeddine, Yessine Hadj Kacem

2025

Abstract

In recent years, credit card transaction fraud has caused significant financial losses for both consumers and financial institutions. To effectively combat these losses, the development of a sophisticated fraud detection system is necessary. However, credit card fraud detection (CCFD) presents significant challenges, particularly in regards to data security and privacy, limiting financial institutions’ ability to share transaction data for model training. This paper introduces the use of Federated Learning for CCFD, a technique that allows for decentralized learning while protecting data privacy. Federated Learning enables multiple institutions to collaborate on model training without having to share sensitive data, effectively addressing privacy concerns. To address the problem of class imbalance in fraud detection datasets, we apply the Synthetic Minority Oversampling Technique (SMOTE) to ensure a balanced dataset. Our study compares Long Short-Term Memory (LSTM) networks to Convolutional Neural Networks (CNN) within a Federated Learning framework. The experimental results demonstrate that combining SMOTE and LSTM in a Federated Learning setup produces superior performance. These findings highlight the strength of LSTM models in processing sequential transaction data and reveal that Federated Learning, when paired with resampling techniques, strengthens fraud detection accuracy.

Paper Citation

in Harvard Style

Mohamedhen W., Charfeddine M. and Hadj Kacem Y. (2025). Enhanced Credit Card Fraud Detection Using Federated Learning, LSTM Models, and the SMOTE Technique. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 368-375. DOI: 10.5220/0013135100003890

in Bibtex Style

@conference{icaart25,
author={Weddou Mohamedhen and Maha Charfeddine and Yessine Hadj Kacem},
title={Enhanced Credit Card Fraud Detection Using Federated Learning, LSTM Models, and the SMOTE Technique},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={368-375},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013135100003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Enhanced Credit Card Fraud Detection Using Federated Learning, LSTM Models, and the SMOTE Technique
SN - 978-989-758-737-5
AU - Mohamedhen W.
AU - Charfeddine M.
AU - Hadj Kacem Y.
PY - 2025
SP - 368
EP - 375
DO - 10.5220/0013135100003890
PB - SciTePress