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Cited by 439Imbalanced Data Classification TechniquesPrivacy-Preserving Technologies in DataFinancial Distress and Bankruptcy Prediction

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

Open access
Alarfaj, Fawaz Khaled, Malik, Iqra, Khan, Hikmat Ullah, Almusallam, Naif, Ramzan, Muhammad, Ahmed, Muzamil·2022·IEEE Access
DOI 10.1109/access.2022.3166891
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Abstract

People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. In this research study, the main aim is to detect such frauds, including the accessibility of public data, high-class imbalance data, the changes in fraud nature, and high rates of false alarm. The relevant literature presents many machines learning based approaches for credit card detection, such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost. However, due to low accuracy, there is still a need to apply state of the art deep learning algorithms to reduce fraud losses. The main focus has been to apply the recent development of deep learning algorithms for this purpose. Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes. The detailed empirical analysis is carried out using the European card benchmark dataset for fraud detection. A machine learning algorithm was first applied to the dataset, which improved the accuracy of detection of the frauds to some extent. Later, three architectures based on a convolutional neural network are applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out by applying variations in the number of hidden layers, epochs and applying the latest models. The evaluation of research work shows the improved results achieved, such as accuracy, f1-score, precision and AUC Curves having optimized values of 99.9%,85.71%,93%, and 98%, respectively. The proposed model outperforms the state-of-the-art machine learning and deep learning algorithms for credit card detection problems. In addition, we have performed experiments by balancing the data and applying deep learning algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-world detection of credit card fraud.

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Metadata

Authors
Alarfaj, F. K., Malik, I., Khan, H. U., Almusallam, N., Ramzan, M., & Ahmed, M.
Affiliations
  • Imam Mohammad ibn Saud Islamic University (SA)
  • University of Sargodha (PK)
  • COMSATS University Islamabad (PK)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022
Volume
10
Pages
39700-39715
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2022.3166891
OpenAlex
W4226492624
Cited by
439
References
43
Citations by year
2026: 96 · 2025: 150 · 2024: 123 · 2023: 60 · 2022: 10
FWCI
28.22
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University through the Research Group (RG-21-51-01)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
Keywords
credit card fraud detection, deep learning, convolutional neural networks, class imbalance, false alarm reduction, online transactions, financial fraud

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