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

arXiv:2402.09830v1 Announce Type: new
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 …

abstract advantages adversarial anomaly anomaly detection application artificial arxiv challenge cs.ai cs.ce cs.lg data detection domains fraud fraud detection gans generative generative adversarial networks identify instances networks normal paper research synthetic training transaction data type