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This is a Experimental Study study.

October 1, 2025Open Access

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions

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The algorithm detects suspicious behaviors in payment flows, enhancing accuracy with generative models.
Using generative adversarial networks, it generates simulated data to improve detection of anomalies in transactions.
A variational autoencoder models latent distributions, ensuring generated data closely matches real transaction features.
Experimental results indicate significant improvement over traditional machine learning and deep learning methods.

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This algorithm detects unusual patterns using generative adversarial networks and variational autoencoders, improving fraud detection.

Tang et al. (2025) studied this question.

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