# Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data

arXiv: https://arxiv.org/abs/2402.09830 · HTML: https://mlsanity.com/paper/2402.09830/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms](https://mlsanity.com/paper/2312.12872/) (2023)
- [Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method](https://mlsanity.com/paper/2401.06782/) (2024)
- [Application analysis of ai technology combined with spiral CT scanning in early lung cancer screening](https://mlsanity.com/paper/2402.04267/) (2024)

## Built on by (most cited first, 5 of 5)

- [Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection](https://mlsanity.com/paper/2403.02232/) (2024)
- [Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems](https://mlsanity.com/paper/2404.00903/) (2024)
- [Improving Credit Card Fraud Detection through Transformer-Enhanced GAN Oversampling](https://mlsanity.com/paper/2509.19032/) (2025)
- [Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis](https://mlsanity.com/paper/2403.17549/) (2024)
- [Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection](https://mlsanity.com/paper/2605.21164/) (2026)

## Abstract (arXiv)

Anomaly detection is a critical challenge across various research domains, aiming to identify instances that deviate from normal data distributions. This paper explores the application of Generative Adversarial Networks (GANs) in fraud detection, comparing their advantages with traditional methods. GANs, a type of Artificial Neural Network (ANN), have shown promise in modeling complex data distributions, making them effective tools for anomaly detection. The paper systematically describes the principles of GANs and their derivative models, emphasizing their application in fraud detection across different datasets. And by building a collection of adversarial verification graphs, we will effectively prevent fraud caused by bots or automated systems and ensure that the users in the transaction are real. The objective of the experiment is to design and implement a fake face verification code and fraud detection system based on Generative Adversarial network (GANs) algorithm to enhance the security of the transaction process.The study demonstrates the potential of GANs in enhancing transaction security through deep learning techniques.