Addressing Credit Card Fraud Detection Challenges with Adversarial Autoencoders
Abstract
1. Introduction
2. Related Work
3. Methods
3.1. Autoencoder
3.2. Generative Adversarial Network (GAN)
3.3. Adversarial Autoencoder
3.4. Other Unsupervised Models
- It enables unsupervised fraud detection without reliance on labeled data.
- It is robust to extreme class imbalance, as it learns exclusively from the majority (normal) class.
- It can detect emerging fraud patterns, effectively addressing concept drift by identifying deviations from learned normal behavior.
- The structured latent space enhances both anomaly separation and interpretability.
4. Experiments
4.1. Dataset
4.2. Model Optimization
- Encoder: 4 layers of 29-256-64-16-4-2,
- Decoder: 4 layers of 2-4-16-64-256-29,
- Discriminator: 4 layers of 2(hidden variable Z)-256-16-4-2-1,
- Learning rate of Encoder, Decoder and Discriminator: e−3, e−3 and e−5,
- Batch size: 128.
4.3. Results
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Evaluation Metrics | Methods | |||
---|---|---|---|---|
Adversarial Autoencoder | Autoencoder | Isolation Forest | One Class SVM | |
Accuracy | 0.9994 | 0.9878 | 0.9931 | 0.9870 |
Precision | 0.8295 | 0.322 | 0.4695 | 0.1646 |
Recall | 0.8008 | 0.791 | 0.4695 | 0.2439 |
F1 Score | 0.8149 | 0.458 | 0.4695 | 0.1966 |
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Ma, S.; Hargreaves, C.A. Addressing Credit Card Fraud Detection Challenges with Adversarial Autoencoders. Big Data Cogn. Comput. 2025, 9, 168. https://doi.org/10.3390/bdcc9070168
Ma S, Hargreaves CA. Addressing Credit Card Fraud Detection Challenges with Adversarial Autoencoders. Big Data and Cognitive Computing. 2025; 9(7):168. https://doi.org/10.3390/bdcc9070168
Chicago/Turabian StyleMa, Shiyu, and Carol Anne Hargreaves. 2025. "Addressing Credit Card Fraud Detection Challenges with Adversarial Autoencoders" Big Data and Cognitive Computing 9, no. 7: 168. https://doi.org/10.3390/bdcc9070168
APA StyleMa, S., & Hargreaves, C. A. (2025). Addressing Credit Card Fraud Detection Challenges with Adversarial Autoencoders. Big Data and Cognitive Computing, 9(7), 168. https://doi.org/10.3390/bdcc9070168