Abstract: Credit card consumption has become an important way of consumption in modern life, but the problem of credit card fraud has also emerged, disrupting the financial order and restricting the development of the industry. Aiming at the data class imbalance problem in credit card fraud detection and improving the accuracy of fraud detection, this paper uses the Generative Adversarial Network (GAN) to generate fraud samples and balance the number of fraud transaction samples and normal transaction samples. Then, a deep neural network (DNN) is used to construct a credit card fraud prediction model. The study compares this model with commonly used classification algorithms and sampling methods in detail and confirms that the designed credit card fraud prediction model has a good effect, providing a theoretical basis and practical reference for financial institutions to predict credit card fraud.

Qin Wang and Mary Jane C.Samonte, “Research on Credit Card Fraud Prediction Model Based on GAN-DNN Imbalance Classification Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151054

@article{Wang2024,
title = {Research on Credit Card Fraud Prediction Model Based on GAN-DNN Imbalance Classification Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151054},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151054},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Qin Wang and Mary Jane C.Samonte}
}