Enhancing Financial Forecasting and Risk Assessment with Artificial Intelligence

This theoretical research paper examines the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance financial forecasting and risk assessment. The study aims to develop innovative models that leverage advanced AI and ML algorithms to predict financial performance and assess associated risks, with the goal of informing strategic investment decisions. The theoretical foundation of the research draws from established frameworks in finance, economics, and computer science, including the efficient market hypothesis, behavioral finance theory, and modern portfolio theory. The developed models utilize a combination of supervised and unsupervised learning techniques, such as recurrent neural networks, random forest classifiers, and clustering algorithms, to capture the complex and dynamic nature of financial markets. The performance of the AI-driven models is evaluated using a range of metrics, including mean squared error, R-squared, and area under the receiver operating characteristic (ROC) curve, demonstrating significant improvements over traditional forecasting and risk assessment methods. The findings of this study contribute to the theoretical understanding of the role of AI in financial decision-making and provide practical insights for investors, financial institutions, and policymakers seeking to leverage these technologies to enhance their financial forecasting and risk assessment capabilities. The research also highlights the limitations and future research directions in this rapidly evolving field, emphasizing the need for continued exploration of the ethical implications and responsible deployment of AI-driven financial modeling.