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Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data
[article]
2024
arXiv
pre-print
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
arXiv:2402.09830v1
fatcat:bnk4exqbn5g4tmepg76kv4lb7i