From Credit Scoring to Market Surveillance: A Review of AI-Driven Financial Risk Management Systems

Abstract

This study presents a comprehensive review of the application of (AI) in financial forecasting, an essential aspect of financial decision-making that shapes individual investment strategies as well as global economic policies. With financial markets becoming increasingly volatile and multifaceted, traditional forecasting models struggle to deliver accurate insights. In contrast, AI-driven models demonstrate significantly enhanced predictive accuracy, computational efficiency, and adaptability. By offering greater speed, precision, and responsiveness, AI has come up as a transformative instrument in financial forecasting and risk assessment, fundamentally reshaping the contribution of finance professionals and fostering greater strategic insight and innovation. Its integration enables organizations to move beyond traditional analytical approaches toward more predictive, data-driven decision-making. Consequently, institutions that adopt AI-enabled forecasting capabilities gain a competitive edge by improving operational efficiency, strengthening risk management, and enhancing long-term financial planning.

 From the perspective of financial risk management, combined with relevant data research, This paper examines the current applications and associated risks of AI in financial risk management, proposes relevant countermeasures and recommendations, and explores future requirements for effective AI deployment in this field.