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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-024-01048-8 |
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| Summary: | 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. |
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| ISSN: | 2196-1115 |