Synthetic Demographic Data Generation for Card Fraud Detection Using GANs

TL;DR

This paper introduces DGGAN, a GAN-based model that generates synthetic demographic data to enhance card fraud detection by addressing class imbalance and enriching transaction data features.

Contribution

The study presents a novel deep learning GAN model for generating synthetic demographic data, improving fraud detection performance over traditional methods.

Findings

DGGAN effectively generates realistic demographic data.

Synthetic data helps mitigate class imbalance in fraud detection.

Enhanced transaction data features improve detection accuracy.

Abstract

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.