How Asset Managers Can Capitalise on Generative AI

How asset managers can capitalise on generative AI

By Timothée Raymond, Head of Innovation & Technology @ Linedata

As with every business, staying abreast of the latest technological advances is pivotal for success. The need for innovation holds especially true for the asset management industry. Ever-tightening margins have meant that firms have less flexibility to grow their headcount in line with the increasing number and complexity of portfolios and funds they offer. To rise to this challenge, generative AI as a partner to human intelligence is emerging as a novel avenue of development. Firms taking up this strategy are set to not only gain an investment edge but also disrupt the industry in unprecedented ways.

Accelerating past automation

While automation has undeniably played a pivotal role in achieving gains in operational efficiency, the ascent of generative AI technology signifies a step-change in sophistication. The implementation of these advanced solutions is gaining traction. In fact, Linedata’s 2023 Global Asset Management Survey found that approximately a third of the industry has embraced and implemented sophisticated generative AI technologies. These tools not only harness internal data but tap into a vast repository of external data for comprehensive portfolio and operational analysis and insights. As an example, they can help diagnose trade failures and provide easy-to-digest, actionable predictive intelligence. Of course, prevention is more cost-effective than retroactive remedies and generative AI can be a vital tool in the arsenal for firms seeking to economise costs and enhance performance by leveraging predictive AI insights.

Additionally, in the middle and back office, AI can help reduce operational risk, by making processes more automated and transparent, and improve regulatory compliance when used to augment human capabilities. For example, take the challenge of compliance officers sifting through numerous false positives in rule-based review systems for suspicious transactions. By introducing user-friendly human-like chatbot interfaces, these employees are empowered to navigate complex machine learning models, quickly verify whether alerts raised are genuine, and use actionable insights for appropriate mitigation strategies enabling them to improve efficiencies in their process.

Empowering alpha generation and talent retention

Generative AI not only possesses the superior computing power of regular AI processes, for example, those used in financial analysis, but has the added advantage of producing outputs that are easier for humans to understand. This feature affords the technology the potential to execute decision-making tasks itself, going beyond AI’s conventional role as a tool for processing and presenting data. This potential to contribute to decision-making positions generative AI as a catalyst for alpha generation within the front office, and as a partner in driving investment operations success.

Many firms are still reliant on manual, paper-based processes. In the face of the economies of scale that generative AI promises, firms that look to grow solely by increasing headcount will struggle to compete with firms that choose to augment their employees’ abilities with the latest tools. Especially in a challenging economic climate, generative AI can be an asset for firms seeking to retain high-value talent. In a similar vein to how automation streamlines repetitive tasks for back-office personnel, generative AI can liberate employees across the board to focus on higher-order tasks and responsibilities, boosting satisfaction and contributing to overall innovation in the company.

Maximising the benefits of hybrid intelligence

Although it may have advanced technological capabilities, asset management firms must adopt a measured and strategic approach to integrating generative AI into their core operations and business-critical tasks. Public AI, where sensitive firm and client data is uploaded to public servers, is yet to develop sufficient privacy guardrails and firms using these tools may inadvertently strengthen their competitors. Moreover, the inherent complexity of financial markets requires judgment, nuance, and contextual understanding that cannot yet be wholly delegated to algorithms. Though managers might now have much quicker access to detailed research reports, the risk of AI “hallucinating” information highlights the need to keep humans in the driver’s seat. Generative AI platforms excel in quickly creating convincing content but are yet to develop the necessary safeguards to ensure the information is accurate. For example, the platforms trained on public data may be influenced by unreliable information available on the internet and these inaccuracies can have costly consequences for traders if the technology isn’t used with the necessary oversight.

Conclusion

To extract the most benefit possible, AI models and the staff using them should receive precise training tailored to their specific use cases as well as continuous data inputs, enabling these innovations to support their staff and business objectives effectively. Additionally, ensuring that the technology is sufficiently private for highly regulated financial organisations is a pressing concern, especially for heavily regulated financial organizations entrusted with sensitive customer data. When used to augment – not replace – human capabilities, the hybrid intelligence of experienced human decision-makers and generative AI can optimise accuracy, reliability, and performance. For asset managers, this approach holds the key to sustaining a competitive advantage in a rapidly evolving landscape.