Paper Type

Complete

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%).

Paper Number

1384

Author Connect URL

https://authorconnect.aisnet.org/conferences/AMCIS2024/papers/1384

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Aug 16th, 12:00 AM

Generative Adversarial Networks in Fraud Detection: A Systematic Literature Review

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%).

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