  Using machine learning models to generate synthetic data has become common in
many fields. Technology to generate synthetic transactions that can be used to
detect fraud is also growing fast. Generally, this synthetic data contains only
information about the transaction, such as the time, place, and amount of
money. It does not usually contain the individual user's characteristics (age
and gender are occasionally included). Using relatively complex synthetic
demographic data may improve the complexity of transaction data features, thus
improving the fraud detection performance. Benefiting from developments of
machine learning, some deep learning models have potential to perform better
than other well-established synthetic data generation methods, such as
microsimulation. In this study, we built a deep-learning Generative Adversarial
Network (GAN), called DGGAN, which will be used for demographic data
generation. Our model generates samples during model training, which we found
important to overcame class imbalance issues. This study can help improve the
cognition of synthetic data and further explore the application of synthetic
data generation in card fraud detection.
