AI-Driven Financial Risk Management: Enhancing Predictive Accuracy and Decision-Making

Abstract

The integration of Artificial Intelligence (AI) into financial risk management has redefined traditional practices by enabling organizations to identify, assess, and mitigate risks with greater efficiency and precision. This paper explores the application of machine learning models, natural language processing, and deep learning techniques in forecasting market volatility, detecting fraudulent transactions, and optimizing portfolio strategies. By leveraging large-scale financial datasets and real-time data streams, AI systems provide early-warning indicators and scenario-based risk assessments that surpass conventional statistical methods. The study presents a comparative analysis of AI-based predictive models against traditional approaches, highlighting improvements in accuracy, adaptability, and speed of decision-making. Additionally, the ethical implications of automated risk management are discussed, emphasizing the importance of transparency, explainability, and regulatory compliance. The findings demonstrate how AI not only strengthens financial resilience but also fosters trust and innovation in the rapidly evolving global economy.