International Research Journal of Education and Technology

The foreign exchange (FX) market is the largest financial arena in the world, processing over $5 trillion daily. While its size supports global trade and investment, it also exposes companies to significant currency risks. Changes in monetary policies, geopolitical events, and economic shocks can quickly erode profits and disrupt financial stability. Traditional hedging tools like forwards and swaps provide some stability but often have a hard time keeping up with today’s fast, interconnected markets.

AI and machine learning are changing FX risk management. Techniques like long short-term memory (LSTM) networks, transformer models, and ensemble learning can capture complex market dynamics better than traditional econometric methods. Evidence shows that AI-driven models can raise forecasting accuracy to as high as 90 to 99 percent in some cases. This improvement allows companies to lower hedging costs, reduce currency losses, and respond to volatility in real time. Reinforcement learning and adaptive algorithms also help treasuries dynamically adjust exposures, protecting profit margins and strengthening resilience.

However, the rise of AI brings new challenges. Its “black box” nature, systemic risks from widespread algorithm use, regulatory concerns, and high adoption costs highlight the need for caution. The study emphasizes that AI should support—not replace—human judgment, with governance, stress-testing, and ethical oversight as key components.

In conclusion, AI is transforming FX risk management from a defensive task into a strategic capability focused on the future. If used responsibly, it can protect companies, reduce inefficiencies, and improve global financial stability.

KEYWORDS: Foreign Exchange Market; Currency Risk; Artificial Intelligence; Machine Learning; Forecasting Models; Hedging Strategies; Reinforcement Learning; Financial Stability.