Artificial Intelligence and Predictive Analytics for Financial Risk Management in Global Markets

Abstract

Financial institutions across the globe are increasingly turning to artificialintelligence (AI)-enabled predictive analytics to navigate risks anduncertainties in dynamic markets. This paper investigates the application ofmachine learning algorithms, natural language processing, and big dataframeworks in predicting financial market volatility, fraud detection, andcredit risk assessment. By analyzing vast datasets including trading patterns,customer transactions, and geopolitical indicators, predictive analytics can provide actionable insights for both investors and policymakers. A unique contribution of this study is its emphasis on integrating unstructured data sources such as news reports, social media sentiment, and macroeconomic policy announcements into intelligent predictive models. The research also discusses the ethical implications of algorithmic trading, systemic risks, and transparency issues in AI-driven financial systems. This comprehensive review provides both a theoretical foundation and practical strategies for adopting predictive analytics in finance, ensuring better resilience in global markets. KEYWORD: Predictive analytics, Artificial intelligence, Financial risk,Market prediction, Big data finance