The Transformative Role of Artificial Intelligence in Financial Decision-Making: Main Applications in Corporate and Personal Finance, Impacts and Future Prospects

The integration of Artificial Intelligence (AI) into financial decision-making is revolutionizing the industry by enhancing analytical precision, expediting decision-making processes, and mitigating human biases. AI-driven models leverage machine learning techniques, deep learning architectures, and advanced statistical methods to extract complex patterns from vast datasets, facilitating more accurate credit scoring, risk assessment, and fraud detection. Furthermore, AI enhances financial inclusivity by utilizing alternative data sources, enabling more personalized and adaptive financial solutions. The deployment of AI-driven advisory systems, robo-advisors, and algorithmic trading strategies has redefined investment management, offering data-driven recommendations and real-time market analysis. However, the widespread adoption of AI also introduces challenges related to algorithmic transparency, ethical considerations, and regulatory compliance. The emergence of explainable AI (XAI) aims to address concerns regarding model interpretability, ensuring that financial institutions maintain accountability and fairness in AI-driven decisions. Moreover, advancements in quantum computing are expected to exponentially enhance computational power, further refining predictive modeling capabilities and risk management strategies. This thesis explores the evolving role of AI in financial decision-making, its transformative impact on risk assessment and financial accessibility, and the future trajectory of AI-driven financial models. Through a comprehensive analysis of current methodologies, regulatory implications, and technological advancements, this study provides insights into the ongoing evolution of AI within the financial sector and its potential to reshape global financial ecosystems.