Utilizing GANs for fraud detection: model training with synthetic transaction data

Mengran Zhu; Yulu Gong; Yafei Xiang; Hanyi Yu; Shuning Huo

doi:10.1117/12.3034346

13 June 2024 Utilizing GANs for fraud detection: model training with synthetic transaction data

Mengran Zhu, Yulu Gong, Yafei Xiang, Hanyi Yu, Shuning Huo

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131803K (2024) https://doi.org/10.1117/12.3034346
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mengran Zhu, Yulu Gong, Yafei Xiang, Hanyi Yu, and Shuning Huo "Utilizing GANs for fraud detection: model training with synthetic transaction data", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131803K (13 June 2024); https://doi.org/10.1117/12.3034346

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KEYWORDS

Gallium nitride

Education and training

Data modeling

Adversarial training

Deep learning

Evolutionary algorithms

Artificial intelligence

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