AI and Finance: Navigating Transformation, Challenges, and Opportunities in Decision-Making

Abstract

Artificial Intelligence (AI) integration has become a catalytic force in reshaping the landscape of financial decision-making. This study embarks on a comprehensive exploration, delving into the intricate interplay between AI and the financial sector by scrutinizing existing literature. The analysis encompasses a broad spectrum of AI applications, ranging from algorithmic trading to risk management, elucidating the transformative benefits such as efficiency gains, data-driven insights, and enhanced predictive capabilities. Despite the promising advantages, ch\allenges emerge in the integration of AI into financial decision-making. These challenges include addressing ethical concerns, mitigating algorithmic biases, and ensuring robust cybersecurity measures. The literature review highlights pivotal contributions, exemplified by Gupta et al.’s exploration of AI’s impact on algorithmic trading, Birch et al.’s insights into AI-driven credit scoring, and Wang et al.’s examination of AI in risk management. Various researchers collectively underscore the nuanced impacts of AI on market efficiency, credit assessments, sentiment analysis, and wealth management within the financial landscape. The benefits derived from AI in financial decision-making encompass heightened accuracy and efficiency, advanced predictive analytics, and the provision of personalized financial services. However, algorithmic bias, interpretability issues, ethical considerations, regulatory compliance, and data security necessitate careful navigation for responsible AI deployment in the f inance sector.