Artificial Intelligence–Enabled Foreign Exchange Risk Management and Volatility Governance in a Globalized Financial System

Abstract

Foreign exchange risk has emerged as one of the most structurally complex and strategically significant challenges confronting multinational corporations, financial institutions, and policy authorities in an era of deep financial globalization, digital transformation, and heightened macroeconomic uncertainty. Volatile capital flows, asymmetric monetary policies, geopolitical shocks, and the increasing speed of information diffusion have fundamentally altered the dynamics of currency markets, rendering traditional foreign exchange risk management approaches insufficient when applied in isolation. Against this backdrop, this research article develops a comprehensive, theoretically grounded, and empirically informed framework for understanding contemporary foreign exchange risk management through the integrated lenses of governance structures, hedging instruments, volatility modeling, and artificial intelligence–driven predictive analytics.

Drawing strictly on the provided academic and institutional references, the article synthesizes insights from central bank intervention literature, corporate governance theory, financial econometrics, and recent advances in machine learning applications for foreign exchange forecasting. It conceptualizes foreign exchange risk not merely as a financial exposure to be minimized, but as a strategic variable embedded within organizational decision-making, capital allocation, and long-term competitiveness. Particular emphasis is placed on the evolution of volatility modeling, including realized volatility, jump components, and intraday market dynamics, and on how these foundational econometric advances inform modern AI-driven forecasting systems.

The study adopts a qualitative, theory-building methodology, systematically integrating insights from peer-reviewed research, policy reports, and practitioner-oriented analyses. Rather than relying on numerical estimation or formal modeling, the article provides an in-depth descriptive examination of methodological approaches, governance mechanisms, and strategic outcomes. The results highlight that firms achieving superior foreign exchange risk outcomes consistently exhibit three interrelated characteristics: robust governance frameworks that align risk management with corporate strategy, diversified and layered hedging approaches combining financial and natural hedges, and advanced analytical capabilities leveraging big data and artificial intelligence.

The discussion critically evaluates the limitations of algorithmic and AI-based approaches, including model risk, data dependency, interpretability challenges, and systemic feedback effects, while also identifying future research directions in hybrid human–machine decision systems and regulatory harmonization. The article concludes that effective foreign exchange risk management in the contemporary environment requires an integrated paradigm that bridges econometric rigor, technological innovation, and institutional governance. By offering an expansive theoretical elaboration grounded in authoritative sources, this research contributes to both academic scholarship and managerial practice in international finance.