Against the backdrop of continuous innovation in financial markets, quantitative trading strategies, characterized by data-driven decision-making, model-based analysis, automated execution, and controllable risk, have exerted a profound impact on insurance investment and risk management. In this context, the application path, technical mechanism and risk governance framework of artificial intelligence in global macro-quantitative investment are systematically discussed, focusing on the structured processing of multi-source heterogeneous data, non-linear factor mining, the role of deep learning and reinforcement learning in strategy generation, and the promotion effect of generative AI on factor construction and strategy iteration. The article further examines the robustness and adaptability of AI models in extreme market conditions, and proposes optimization strategies that take into account theory and practice for key links such as high-end interdisciplinary team construction, systematic research platform, intelligent data processing architecture, and risk management closed loop, and provides a systematic reference for realizing the intelligence, dynamics, and risk control of quantitative global macro investment.

Sitong Diao. Artificial Intelligence in Quantitative Global Macro Investing: Implementation Scenarios, Practical Challenges, and Future Trends. Academic Journal of Business & Management (2026), Vol. 8, Issue 2: 106-114. https://doi.org/10.25236/AJBM.2026.080214.

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