The detection of fraudulent activities in credit cards transactions presents a significant challenge due to the constantly changing and unpredictable tactics used by fraudsters, who take advantage of technological advancements to evade security measures and cause substantial financial harm. In this paper, we suggested a machine learning based methodology to detect fraud in credit cards. The suggested method contains four key phases, including data normalization, data preprocessing, feature selection, classification. For classification artificial neural network, decision tree, logistic regression, naive bayes, random forest while for feature selection particle swarm optimization is employed. With the use of a dataset created from European cardholders, the suggested method was tested. The experimental results show that the suggested method beats the other machine learning techniques and can successfully classify frauds with a high detection rate.
Credit card fraud detection machine learning particle swarm optimization
Birincil Dil | İngilizce |
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Konular | Bilgi Sistemleri (Diğer) |
Bölüm | Research Article |
Yazarlar | |
Erken Görünüm Tarihi | 7 Nisan 2024 |
Yayımlanma Tarihi | 14 Haziran 2024 |
Gönderilme Tarihi | 15 Eylül 2023 |
Kabul Tarihi | 30 Ekim 2023 |
Yayımlandığı Sayı | Yıl 2024 |
Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.