Generative AI in Investment and Portfolio Management: Comprehensive Review of Current Applications and Future Directions

Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of portfolio and investment management. This paper provides a comprehensive survey of GenAI applications in the industry, systematically reviewing over 50 contemporary sources. We analyze use cases in portfolio optimization, risk mitigation, client personalization, and operational efficiency. The paper also discusses implementation challenges, ethical considerations, and future research directions. Our findings suggest that GenAI adoption is poised to deliver significant competitive advantages, but also introduces new risks that require careful governance. This paper explores the transformative impact of generative artificial intelligence (GenAI) on investment and portfolio management. We survey current applications across wealth management, asset allocation, risk assessment, and client servicing. The analysis draws from industry reports, academic research, and case studies to present a comprehensive view of how GenAI is reshaping financial services. Key findings include the technology’s ability to enhance decision-making, improve operational efficiency, and enable personalization at scale. We also examine challenges related to transparency, regulation, and implementation. The paper concludes with recommendations for practitioners and directions for future research. The analysis of litreature reveals measurable improvements: 15-20% reductions in portfolio volatility, 30% faster rebalancing cycles, and 40% efficiency gains in client onboarding. However, significant challenges persist, including model interpretability barriers, data quality requirements, and evolving regulatory frameworks. The paper projects that AI-native strategies will dominate institutional investing by 2030, with emerging trends like agentic AI and federated learning reshaping the competitive landscape. Financial institutions must balance innovation with robust governance, as the technology’s $1 trillion market potential hinges on addressing transparency and ethical concerns. This comprehensive review synthesizes insights from academic research, industry case studies, and quantitative analyses to guide practitioners through GenAI adoption while identifying critical gaps for future research in long-term performance metrics and comparative solution analysis. This is a review paper and all results quoted are from their respective sources.