Exploring the Transformative Role of Generative AI in Financial Forecasting and Advanced Fraud Detection Strategies

This paper investigates the transformative impact of generative artificial intelligence (GenAI) in financial forecasting and fraud detection, using a review approach to combine previous research and actual applications. Generative AI, which includes models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based architectures like GPT, is a paradigm shift in data-driven decision-making. The assessment emphasizes GenAI's advantages over traditional financial models, specifically its capacity to generate realistic synthetic data, find complicated patterns, and adapt to changing trends. GenAI plays an important role in fraud detection by simulating adversarial behaviors and improving anomaly detection systems. This paper highlights GenAI's ability to solve the constraints of traditional models, such as static rule-based systems and linear forecasting methodologies, and presents a thorough framework for incorporating generative AI solutions into financial operations. Future research recommendations are also highlighted, as are ethical considerations for employing GenAI in financial systems.