GAN-based Data Augmentation for Credit Card Fraud Detection

Authors: Strelcenia, E. and Prakoonwit, S.

Journal: Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Pages: 6812-6814

DOI: 10.1109/BigData55660.2022.10020419

Abstract:

Deep generative approaches, such as GANs (generative adversarial networks), can be used to efficiently generate new data points that are similar to existing ones. This can be useful for increasing the size of a dataset or for creating synthetic data points that can be used in place of real ones. In this study, we trained classifiers using our novel K-CGAN approach and compared them to other oversampling approaches. We achieved higher F1 score performance metrics than the other methods. After conducting several experiments, we found that classifiers based on a Random Forest, Nearest Neighbor, Logistic Regression, MLP or Adaboost algorithm trained on the augmented set performed much better than those trained on the original data. This effectively creates a fraud detection mechanism.

https://eprints.bournemouth.ac.uk/38333/

Source: Scopus

GAN-based Data Augmentation for Credit Card Fraud Detection

Authors: Strelcenia, E. and Prakoonwit, S.

Pages: 6812-6814

Publisher: IEEE

Place of Publication: New York, NY: USA

ISBN: 9781665480451

Abstract:

Deep generative approaches, such as GANs (generative adversarial networks), can be used to efficiently generate new data points that are similar to existing ones. This can be useful for increasing the size of a dataset or for creating synthetic data points that can be used in place of real ones. In this study, we trained classifiers using our novel K-CGAN approach and compared them to other oversampling approaches. We achieved higher F1 score performance metrics than the other methods. After conducting several experiments, we found that classifiers based on a Random Forest, Nearest Neighbor, Logistic Regression, MLP or Adaboost algorithm trained on the augmented set performed much better than those trained on the original data. This effectively creates a fraud detection mechanism.

https://eprints.bournemouth.ac.uk/38333/

Source: BURO EPrints