1. Accelerating digitalization
AI will accelerate the digitalization of asset managers’ relationships with investors and advisors. AI will power the client contact center of the future with self-driving operations by digitizing the majority of cross-channel interactions. As we look to realize the vision, clients can work toward AI-assisted operations, guiding staff with insights during client interactions and deploying personalized chatbots across engagement channels that can provide contextual answers to client queries. A recent EY survey found that 62% of consumers trust AI-generated responses to their questions (via chatbots or automated responses1). Furthermore, GenAI could be combined with customer data and human insights to enhance personalization and build engagement through next generation hybrid distribution models.
2. Portfolio management
There is huge scope for AI to augment human skills, achieving a step-change in firms’ ability to derive risk and performance insights from internal and external data sets. Potential applications could include generating investment signals from large volumes of unstructured data; carrying out advanced pattern recognition; and calculating time-weighted return predictions or risk outcome simulations. For example, analysts could use a human language interface to search satellite images for investment insights.
3. Automation and efficiency
AI is enhancing processes across the front, middle and back offices, with practical applications that include automating investment operations and client onboarding, performing compliance reviews and generating reports, and improving the performance of sales and marketing teams. For example, AI can be used to significantly enhance the detection of money laundering and financial crime. AI will eventually transcend the industry’s current technology silos, with dramatic effects on efficiency and productivity.
Despite this potential, many asset managers are discovering that there is a big difference between experimenting with AI and implementing it at scale. Nor is this a purely practical challenge. If asset managers are to succeed with AI, their clients and staff will need to trust the technology and understand that its goal is to augment people, not replace them.
To get full value from AI, asset managers will need to get three things right.
First, firms need to establish a strong risk and governance infrastructure around their AI-driven activities. This includes:
- A tailored oversight framework that monitors AI performance and risks, minimizing errors and ensuring full compliance with standards and regulation.
- A particular focus on the heightened risks — such as data security, hallucinations, “explainability,” confidentiality breaches or copyright infringement — that could arise from the use of GenAI.
- Strong operational guardrails that ensure AI governance controls are embedded in the processes, including setting-up operational playbooks to ensure AI does what it’s supposed to do.
- Appropriate staff training, including coaching employees who use GenAI on what questions to ask, how to ask them and how to interpret the responses.