Generative adversarial networks for data augmentation and transfer in credit card fraud detection

Abstract

Augmenting a dataset with synthetic samples is a common processing step in machine learning with imbalanced classes to improve model performance. Another potential benefit of synthetic data is the ability to share information between cooperating parties while maintaining customer privacy. Often overlooked, however, is how the distribution of the data affects the potential gains from synthetic data augmentation. We present a case study in credit card fraud detection using Generative Adversarial Networks to generate synthetic samples, with explicit consideration given to customer distributions. We investigate two different cooperating party scenarios yielding four distinct customer distributions by credit quality. Our findings indicate that institutions skewed towards higher credit quality customers are more likely to benefit from augmentation with GANs. Relative gains from synthetic data transfer, in the absence of feature set heterogeneity, also appear to asymmetrically favour banks operating on the lower end of the credit spectrum, which we hypothesise is due to differences in spending behaviours.

Keywords:

• Synthetic data
• fraud detection
• generative adversarial networks
• machine learning
• transfer learning
• distributional analysis

Acknowledgements

The authors acknowledge Research Computing at The University of Virginia for providing computational resources and technical support that have contributed to the results reported within this publication. URL: https://rc.virginia.edu

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This material is based upon work supported by the National Science Foundation under Grant No. CNS: 1650512. This work was conducted in the Center for Visual and Decision Informatics, a National Science Foundation Industry/University Cooperative Research Center.