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AI-Powered Foreign Exchange Risk Management: Comparing Traditional vs. Machine Learning Approaches

Year: 2024

Type: article

Abstract: Foreign exchange (FX) risk management has evolved significantly with the advent of artificial intelligence and machine learning technologies. This research paper examined the comparative effectiveness of traditional risk management approaches versus modern machine learning methodologies in managing foreign exchange exposure. Through analysis of risk measurement techniques, hedging strategies, and predictive capabilities, this study demonstrates that while traditional approaches provide foundational risk management frameworks, machine learning methods offer enhanced accuracy in volatility prediction, dynamic hedging optimization, and real-time risk assessment. The findings suggest that hybrid approaches combining traditional financial theory with machine learning capabilities provide optimal risk management outcomes for organizations operating in global markets.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17113328

Authors: Onohwakpo, Ejiroghene Amanda, Onyeanuforo, Ugochi Linda

Language: English

Topic: Risk Management in Financial Firms

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W7081653680

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