A Comprehensive Review of Generative AI Adoption in Hedge Funds: Trends, Use Cases, and Challenges

This paper provides a comprehensive review of the rapidly evolving landscape of generative AI applications in hedge funds. We analyze 50 recent sources (2022-2025) to identify key trends, use cases, performance impacts, and risks associated with AI adoption in alternative investments. Our review reveals that hedge funds leveraging generative AI achieve 3-5\% higher annualized returns compared to non-adopters, with the most significant benefits in equity hedge strategies. We categorize applications into alpha generation, operational efficiency, risk management, and investor relations, while also examining regulatory concerns and market stability implications. The paper concludes with recommendations for responsible AI adoption and future research directions. We also discuss the challenges associated with integrating GenAI, including data transparency, model explainability, and concentration risks. This paper summarize recent proposals a modular data architecture for the integration of Generative AI (GenAI) in hedge fund investment and risk management workflows. Leveraging a multi-layered design, the framework addresses key stages such as data ingestion, feature engineering, AI model deployment, and end-user delivery through APIs and reporting tools. We distinguish between structured and unstructured data streams and incorporate governance and explainability mechanisms into model oversight. Our system diagram illustrates the vertical flow from raw market data to decision-making outputs, while a complementary citation-source distribution pie chart contextualizes the breadth of supporting literature. The proposed architecture aims to enhance signal generation, operational efficiency, and compliance within AI-augmented asset management, as supported by real-world references and design best practices. This work contributes to the growing body of applied AI systems in finance, offering a blueprint for scalable, auditable, and investor-aligned GenAI deployments. This is pure review paper and all proposals are from cited literature.