Comparison of SVM kernels in Credit Card Fraud Detection using GANs

The technological evolution in smartphones and telecommunication systems have led people to be more dependent on online shopping and electronic payments, which created burdensome task of transaction validation for many financial institutions. This paper examined and evaluated the efficacy of Support vector machine (SVM) kernels on Generative Adversarial Network (GAN)-generated synthetic data to detect credit card fraud transactions. Four SVM kernels have been investigated and compared; linear, polynomial, sigmoid, and redial basis function. The accuracy results indicated that linear and polynomial kernels reached over 91%, while sigmoid and redial basis function reached 79% and 83% respectively. Linear and polynomial models received over 90% ROC and F1 score, in contrast the ROC scores were lower for sigmoid (81%) and redial basis function (83%). Both sigmoid and redial basis function achieved over 80% in terms of F1 score. The precision score demonstrated a high score for both linear and polynomial kernel reaching 99%. Additionally, sigmoid and redial basis function achieved over 80%. These results overcame the imbalance dataset issue through the generation of synthetic data by applying the SVM kernels using GANs algorithm.

@article{Alshawi2024,
  title     = {Comparison of SVM kernels in Credit Card Fraud Detection using GANs},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Bandar Alshawi},
  doi       = {10.14569/IJACSA.2024.0150131},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150131}
}