AI in Finance: Enhancing Risk Management and Investment Decisions

Abstract

Artificial Intelligence (AI) is transforming the financial sector, particularly in the domains of risk management and investment decision-making. The ability of AI to process vast amounts of data, detect patterns, and make real-time predictions has enhanced the accuracy and efficiency of financial operations. AI-powered risk management systems utilize machine learning algorithms to analyze historical data, assess market fluctuations, and identify potential financial risks with greater precision than traditional models (Goodell et al., 2021). Moreover, AI-driven predictive analytics assist investors in making informed decisions by evaluating market trends, forecasting asset performance, and mitigating uncertainties (Bussmann et al., 2020). Automated trading strategies, powered by AI, improve portfolio management by adjusting asset allocations based on real-time data and risk tolerance levels (Feng et al., 2018). The integration of AI in finance also strengthens fraud detection mechanisms, ensuring enhanced security and compliance with regulatory requirements (Zhang et al., 2022). Despite these advancements, challenges such as ethical concerns, algorithmic biases, and data privacy issues remain significant obstacles (Kou et al., 2021). This research explores the impact of AI on financial risk management and investment strategies, highlighting the potential benefits and challenges associated with AI-driven financial decision-making. The findings suggest that while AI offers remarkable advantages, a balanced approach integrating human oversight and regulatory frameworks is essential for responsible AI deployment in finance.