• Hari Prasad Josyula

In the evolving landscape of financial analysis, Generative Artificial Intelligence (AI) has emerged as a pivotal tool for deriving predictive insights from vast historical data. This paper explores the transformative role of Generative AI in financial forecasting, focusing on its capacity to unveil future market trends and investment opportunities. Employing methodologies like Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs), the study demonstrates the enhanced predictive accuracy of these models compared to traditional financial analysis techniques. Key findings reveal Generative AI's potential in stock market prediction, credit risk analysis, and fraud detection, offering a broader and more dynamic perspective for financial decision-making. The paper also addresses the ethical considerations and challenges inherent in deploying AI in sensitive financial contexts. These insights underscore the growing significance of Generative AI in shaping future financial strategies and its potential to revolutionize risk management, personalized financial services, and regulatory compliance. This study contributes to the burgeoning field of AI in finance, highlighting the need for ethical deployment and offering a roadmap for future research and application in this domain.

Generative Artificial Intelligence; Financial Forecasting; Predictive Analytics; Historical Data Analysis; Generative Adversarial Networks (GANs); Recurrent Neural Networks (RNNs); Financial Data Modeling; AI in Finance; Ethical Considerations in AI; Future Trends in Financial Markets

"Predictive Financial Insights with Generative AI: Unveiling Future Trends from Historical Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.e354-e360, January-2024, Available :http://www.jetir.org/papers/JETIR2401442.pdf

"Predictive Financial Insights with Generative AI: Unveiling Future Trends from Historical Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppe354-e360, January-2024, Available at : http://www.jetir.org/papers/JETIR2401442.pdf