Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions
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Abstract
Deep learning (DL), a branch of machine learning (ML), is the core technology in today’s technological advancements and innovations. Deep learning-based approaches are the state-of-the-art methods used to analyse and detect complex patterns in large datasets, such as credit card transactions. However, most credit card fraud models in the literature are based on traditional ML algorithms, and recently, there has been a rise in applications based on deep learning techniques. This study reviews the recent DL-based literature and presents a concise description and performance comparison of the widely used DL techniques, including convolutional neural network (CNN), simple recurrent neural network (RNN), long…
Citation impact
141
total citations
- FWCI
- 44.56
- Percentile
- 100%
- References
- 129
Citations per year
Authors
2Topics & keywords
Topics
Keywords
- Credit card fraud
- Deep learning
- Computer science
- Artificial intelligence
- Machine learning
- Credit card
- Convolutional neural network
- Robustness (evolution)
UN Sustainable Development Goals
- Peace, Justice and strong institutions
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