Generative Adversarial Networks in Fraud Detection: A Systematic Literature Review
Outlet Title
AMCIS 2024 Proceedings
Document Type
Conference Proceeding
Publication Date
2024
Abstract
Fraud detection has become an important part of modern society. Traditional fraud detection techniques struggle with unbalanced data because of the lack of data representing fraudulent behaviors. Recently, Generative Adversarial Networks (GANs) have attracted much attention in producing synthetic data to balance a dataset. In this paper, we present a systematic review of the literature in the area of GAN applications in fraud detection. This paper analyzes the relationships between fraud detection and GANs, and identifies the roles of GAN usage regarding fraud detection: feature-based (85%) and image-based (15%). In addition, the most used GAN architecture is the standard vanilla GAN (37.5%), and the most common fraud aspects for GAN applications are credit card fraud (42.5%), financial fraud (15%), and identity fraud (15%).
Recommended Citation
Wang, Man and El-Gayar, Omar F., "Generative Adversarial Networks in Fraud Detection: A Systematic Literature Review" (2024). Research & Publications. 401.
https://scholar.dsu.edu/bispapers/401