Authors:
Ibtissam Medarhri
1
;
Mohamed Hosni
2
;
Mohamed Ettalhaoui
2
;
Zakaria Belhaj
1
and
Rabie Zine
3
Affiliations:
1
MMCS Research Team, LMAID, ENSMR-Rabat, Morocco
;
2
MOSI Research Team, LM2S3, ENSAM, Moulay Ismail University of Meknes, Meknes, Morocco
;
3
School of Science and Engineering, Al Akhawayn University in Ifrane, Ifrane, Morocco
Keyword(s):
Credit Card Fraud, Machine Learning, Classification, Systematic Mapping Study.
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 sele
cted 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.
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