Generative Models AI Models for Personalized Content Generation in Financial Industry

Abstract

This study provides a comprehensive review of state-of-the-art generative artificial intelligence (AI) models and their applications in the personalized generation of content, especially in the financial field. The study focuses on some key generative methods such as Generative Adversarial Networks (GANs), Transformer-based models, Diffusion models, Variational Autoencoders (VAEs), and their architecture, operation, advantages and limitations. A comparative analysis is given to help or understand their suitability in different use cases in terms of quality, complexity, diversity, and computational requirements. Furthermore, standard evaluation metrics for text, image, and audio generation are discussed in the paper, e.g. Perplexity, BLEU, ROUGE, METEOR, Frechet Inception Distance (FID), and Inception Score (IS), as well as personalization-specific measures. Key challenges such as data privacy, ethical bias, scalability, and computational complexity are critically analyzed in order to gain a range various practical constraints of implementing generative AI systems. The review concludes by stating that while GANs and Diffusion models are good at creating high-quality content, Transformers are great for tasks that involve sequential and text-based information while VAEs are stable and interpretable representations. While there are currently limitations, generative AI has a considerable opportunity to make finance personalized services, automation, and decision-making much more effective. Future developments in model efficiency, robustness and ethical practices in AI are expected to take it even further, in terms of its real world applicability.