A systematic review of AI-enhanced techniques in credit card fraud detection
Abstract The rapid increase of fraud attacks on banking systems, financial institutions, and even credit card holders demonstrate the high demand for enhanced fraud detection (FD) systems for these attacks. This paper provides a systematic review of enhanced techniques using Artificial Intelligence...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-024-01048-8 |
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| author | Ibrahim Y. Hafez Ahmed Y. Hafez Ahmed Saleh Amr A. Abd El-Mageed Amr A. Abohany |
| author_facet | Ibrahim Y. Hafez Ahmed Y. Hafez Ahmed Saleh Amr A. Abd El-Mageed Amr A. Abohany |
| author_sort | Ibrahim Y. Hafez |
| collection | DOAJ |
| description | Abstract The rapid increase of fraud attacks on banking systems, financial institutions, and even credit card holders demonstrate the high demand for enhanced fraud detection (FD) systems for these attacks. This paper provides a systematic review of enhanced techniques using Artificial Intelligence (AI), machine learning (ML), deep learning (DL), and meta-heuristic optimization (MHO) algorithms for credit card fraud detection (CCFD). Carefully selected recent research papers have been investigated to examine the effectiveness of these AI-integrated approaches in recognizing a wide range of fraud attacks. These AI techniques were evaluated and compared to discover the advantages and disadvantages of each one, leading to the exploration of existing limitations of ML or DL-enhanced models. Discovering the limitation is crucial for future work and research to increase the effectiveness and robustness of various AI models. The key finding from this study demonstrates the need for continuous development of AI models that could be alert to the latest fraudulent activities. |
| format | Article |
| id | doaj-art-cc3d17ea1e084f4792822de002d9ce5e |
| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-cc3d17ea1e084f4792822de002d9ce5e2025-01-19T12:26:38ZengSpringerOpenJournal of Big Data2196-11152025-01-0112113510.1186/s40537-024-01048-8A systematic review of AI-enhanced techniques in credit card fraud detectionIbrahim Y. Hafez0Ahmed Y. Hafez1Ahmed Saleh2Amr A. Abd El-Mageed3Amr A. Abohany4Department of Computer Science and Engineering, Faculty of Engineering, Egypt-Japan University of Science and TechnologyDepartment of Electronics and Communication Engineering, Faculty of Engineering, Egypt-Japan University of Science and TechnologyFaculty of Computer and Information, Damanhur UniversityDepartment of Information Systems, Sohag UniversityFaculty of Computer and Information, Damanhur UniversityAbstract The rapid increase of fraud attacks on banking systems, financial institutions, and even credit card holders demonstrate the high demand for enhanced fraud detection (FD) systems for these attacks. This paper provides a systematic review of enhanced techniques using Artificial Intelligence (AI), machine learning (ML), deep learning (DL), and meta-heuristic optimization (MHO) algorithms for credit card fraud detection (CCFD). Carefully selected recent research papers have been investigated to examine the effectiveness of these AI-integrated approaches in recognizing a wide range of fraud attacks. These AI techniques were evaluated and compared to discover the advantages and disadvantages of each one, leading to the exploration of existing limitations of ML or DL-enhanced models. Discovering the limitation is crucial for future work and research to increase the effectiveness and robustness of various AI models. The key finding from this study demonstrates the need for continuous development of AI models that could be alert to the latest fraudulent activities.https://doi.org/10.1186/s40537-024-01048-8Fraud attacksFraud detection (FD)Credit card fraud detection (CCFD)Machine learning (ML)Deep learning (DL)Meta-heuristic optimization (MHO) |
| spellingShingle | Ibrahim Y. Hafez Ahmed Y. Hafez Ahmed Saleh Amr A. Abd El-Mageed Amr A. Abohany A systematic review of AI-enhanced techniques in credit card fraud detection Journal of Big Data Fraud attacks Fraud detection (FD) Credit card fraud detection (CCFD) Machine learning (ML) Deep learning (DL) Meta-heuristic optimization (MHO) |
| title | A systematic review of AI-enhanced techniques in credit card fraud detection |
| title_full | A systematic review of AI-enhanced techniques in credit card fraud detection |
| title_fullStr | A systematic review of AI-enhanced techniques in credit card fraud detection |
| title_full_unstemmed | A systematic review of AI-enhanced techniques in credit card fraud detection |
| title_short | A systematic review of AI-enhanced techniques in credit card fraud detection |
| title_sort | systematic review of ai enhanced techniques in credit card fraud detection |
| topic | Fraud attacks Fraud detection (FD) Credit card fraud detection (CCFD) Machine learning (ML) Deep learning (DL) Meta-heuristic optimization (MHO) |
| url | https://doi.org/10.1186/s40537-024-01048-8 |
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