Utilizing GANs for Fraud Detection: Model Training with Synthetic
  Transaction Data
Mengran Zhu Yulu Gong Yafei Xiang Hanyi Yu Shuning Huo
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.