GENERATIVE AI IN REAL-TIME E-COMMERCE FRAUDDETECTION: A COMPARATIVE AND ETHICAL ANALYSIS

This paper will discuss generative AI models, such as GANs, VAEs, and a hybrid GAN-VAE, towards real-time fraud detection in e-commerce. GANs perform outstandingly in synthesizing fraudulent data, while VAEs detect complex patterns of fraud. The proposed hybrid method combines the respective strengths and achieves higher accuracy and adaptability. Also, issues regarding ethical concerns related to privacy in data and biases are taken care of, illustrating the future possibility of generative AI in upgrading the frameworks for fraud detection in ecommerce. Keywords—Generative AI, Generative Adversarial Networks, Variational Autoencoders, Machine Learning, E-Commerce Fraud

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