Here are our top four use cases for generative AI in asset management:
- Data and predictive analytics
Predictive analytics plays a vital role in asset management. Generative AI empowers asset managers by enhancing foresight through the analysis of vast amounts of data to identify patterns and trends and generating insights. It’s use cases are wide-ranging and applicable to both liquid and illiquid portfolios. It offers the potential for real-time insights on market or portfolio dynamics, assessing the implications of shock events, and providing a detailed understanding of the forces influencing asset values. This real-time, tailored analysis enables asset managers to make proactive, well-informed decisions, adjusting strategies in response to market disruptors and taking advantage of emerging drivers. As accessibility to data and increasingly sophisticated and accurate models becomes more of a market standard, the ability to navigate complexities and seize opportunities grows exponentially. - Portfolio enhancing and generation
AI has the capacity to improve and create investment portfolios by assessing factors like risk tolerance, return expectations, investment objectives, and dynamic market conditions. Its ability to process large datasets to identify optimal asset allocations based on user preferences and market conditions, and ultimately assist in the selection of the most appropriate investment, means that it has the potential to fundamentally augment how we construct portfolios or design investment products. In contrast to traditional portfolio techniques that rely primarily on historical data and assumptions, generative AI enables asset managers to make better-informed investment decisions by offering a dynamic approach to crafting portfolios. - Investor communication and reporting
Generative AI is already being widely used in investor communication within chat bots and CRM systems, and more recently built into investors portals and specialist systems, acting as an easy-to-use query engine, allowing investors to access data and analytics using natural language to communicate with the system. However, improvements in the models and innovative ideas are set to fundamentally augment how the industry communicates with investors, and allows far more dynamic and and customised reporting solutions to be created. - Operational automation
Generative AI for automation is set to play a key role in the continuing technological transformation of investment ops. To capture its value in operations, it should be deployed as a digital transformation, not merely a technological advance – resulting in wide ranging changes to processes, rather than merely augmenting individual tasks. By leveraging AI including generative models, data processing, data analysis, routine tasks, and reporting processes can be automated, leading to significant improvements in overall operational efficiency. AI can streamline repetitive tasks, facilitating faster and more accurate data processing, reducing manual work, and reducing the risk of errors. Automating routine operations not only saves time and resources but also empowers asset management firms to allocate human resources more strategically, focusing on higher-value tasks such as strategic planning, decision-making, and client engagement.
By harnessing the power of generative AI, asset management firms can unlock a multitude of significant benefits. This can provide a competitive edge by enhancing decision-making processes, portfolio management strategies, and delivering superior outcomes, all while ensuring regulatory compliance and mitigating operational risks.
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To find out more, download the latest research report: Embracing change: How digitisation is shaping asset management. This PitchBook report provides actionable insights and strategies for navigating the evolving digital landscape of asset management.
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