Financial Risk Management Enhanced by AI: Predictive Accuracy and Application Scenarios

Abstract

Artificial Intelligence has transformed financial risk management through more prediction accuracy and real-time decision-making. Whereas traditional risk management models struggle to change with the markets' volatility, AI-driven models, such as machine learning or natural language processing models, approach such problems using data to offer insight into risk assessment and optimized investments. This paper will apply AI in financial risk management using recent market data such as stock indices, foreign exchange rates, commodity prices, and bond yields. The study shows that AI improves market forecasting, determines where the financial risks are, and lessens the uncertainties caused by the interpretation of historical patterns and real-time market fluctuation. The results show that AI-powered models enhance predictions of risks about stock market volatility, changes in currency rates, and movements of commodity prices. Moreover, AI-powered sentiment analysis tools offer investors a way to understand investor behavior and trends in macroeconomic scenarios. But with all the challenges like data privacy concerns, regulatory compliance, and something to do with model interpretability. The focus of this study is to predict how AI can revolutionize the financial risk management process, and we highlight the determinants for the safe application of such valuable technology in the financial sector. As the future unfolds, AI-driven AI-driven risk assessment models, such as explainable AI models and quantitative computing, will continue to be improved, making it simply unavoidable for financial institutions and investors to rely on such AI-driven risk assessment models.