Reviewing Machine Learning Techniques in Credit Card Fraud Detection

Ibtissam Medarhri, Mohamed Hosni, Mohamed Ettalhaoui, Zakaria Belhaj, Rabie Zine

2024

Abstract

The growing use of credit cards for transactions has increased the risk of fraud, as fraudsters frequently attempt to exploit these transactions. Consequently, credit card companies need decision support systems that can automatically detect and manage fraudulent activities without human intervention, given the vast volume of daily transactions. Machine learning techniques have emerged as a powerful solution to address these challenges. This paper provides a comprehensive overview of the knowledge domain related to the application of machine learning techniques in combating credit card fraud. To achieve this, a review of published work in academic journals from 2018 to 2023 was conducted, encompassing 131 papers. The review classifies the studies based on eight key aspects: publication trends and venues, machine learning approaches and techniques, datasets, evaluation frameworks, balancing techniques, hyperparameter optimization, and tools used. The main findings reveal that the selected studies were published across various journal venues, employing both single and ensemble machine learning approaches. Decision trees were identified as the most frequently used technique. The studies utilized multiple datasets to build models for detecting credit card fraud and explored various preprocessing steps, including feature engineering (such as feature extraction, construction, and selection) and data balancing techniques. Python and its associated libraries were the most commonly used tools for implementing these models.

Paper Citation

in Harvard Style

Medarhri I., Hosni M., Ettalhaoui M., Belhaj Z. and Zine R. (2024). Reviewing Machine Learning Techniques in Credit Card Fraud Detection. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 179-187. DOI: 10.5220/0013072500003838

in Bibtex Style

@conference{kdir24,
author={Ibtissam Medarhri and Mohamed Hosni and Mohamed Ettalhaoui and Zakaria Belhaj and Rabie Zine},
title={Reviewing Machine Learning Techniques in Credit Card Fraud Detection},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={179-187},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013072500003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Reviewing Machine Learning Techniques in Credit Card Fraud Detection
SN - 978-989-758-716-0
AU - Medarhri I.
AU - Hosni M.
AU - Ettalhaoui M.
AU - Belhaj Z.
AU - Zine R.
PY - 2024
SP - 179
EP - 187
DO - 10.5220/0013072500003838
PB - SciTePress