# Improving Detection of Credit Card Fraudulent Transactions using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1907.03355 · HTML: https://mlsanity.com/paper/1907.03355/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection](https://mlsanity.com/paper/2108.02501/) (2021)
- [Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis](https://mlsanity.com/paper/2408.03441/) (2024)
- [Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced Problem](https://mlsanity.com/paper/2104.03428/) (2021)
- [Improving Credit Card Fraud Detection with an Optimized Explainable Boosting Machine](https://mlsanity.com/paper/2602.06955/) (2026)

## Abstract (arXiv)

In this study, we employ Generative Adversarial Networks as an oversampling method to generate artificial data to assist with the classification of credit card fraudulent transactions. GANs is a generative model based on the idea of game theory, in which a generator G and a discriminator D are trying to outsmart each other. The objective of the generator is to confuse the discriminator. The objective of the discriminator is to distinguish the instances coming from the generator and the instances coming from the original dataset. By training GANs on a set of credit card fraudulent transactions, we are able to improve the discriminatory power of classifiers. The experiment results show that the Wasserstein-GAN is more stable in training and produce more realistic fraudulent transactions than the other GANs. On the other hand, the conditional version of GANs in which labels are set by k-means clustering does not necessarily improve the non-conditional versions of GANs.