Genus has been a disciplined systematic investor for many years, having leveraged quantitative analysis, mathematical models and computer algorithms to make well-informed, data-backed investment decisions. Now we’re exploring new ways to harness Generative AI advancements to enhance portfolio management and the client experience.
AI has been around for three-quarters of a century – existing long before ChatGPT entered the scene – and we’ve been using quantitative techniques in finance for decades. Now investors are beginning to see for themselves the many advantages AI can bring – especially when used by wealth management firms to accelerate data evaluation, reinforce risk mitigation and improve overall productivity.
On April 17, we’ll host a lively discussion on the current status of AI development, its impact on the financial markets, and its role at Genus. We’ll hear from our Executive Chair Person & Chief Investment Officer, Wayne Wachell, Chief Growth Officer, Shannon Ward, and special guest Pavan Mirla, an AI consultant and machine learning data scientist with extensive experience in the financial sector. Here’s a sneak peek of what our conversation will cover.
A bit of AI history
If you’re old enough to remember the rotary phone, you probably remember AI as the stuff of science fiction. AI has long played a role in pop culture – often via sentient robots, cyborg assassins and world dominating villains – but it’s only recently entered the discourse of everyday life via easily accessible tools such as ChatGPT.
AI history, however, goes back centuries, and the mechanics behind artificial intelligence began to really take shape in the early 1950s. Throughout the following decades, AI research progressed, but widespread expansion was limited due to a lack of infrastructure and data. “AI has always had AI winters, when its evolution suddenly stopped,” Mirla says. “But all of a sudden in 2012, AI had a pivotal year.” That’s when Geoffrey Hinton, a British-Canadian computer scientist researching AI at the University of Toronto and through his work with Google met up with Jensen Huang, the CEO of Nvidia. “This effectively established Canada as a Mecca of AI,” Mirla says.
In 2017, we saw more acceleration, when Google introduced the concept of transformers – a type of deep learning model used primarily for natural language processing. “Transformers keep track of past learnings very clearly,” Mirla says, adding, they were the precursor for ChatGPT, or Generative Pre-trained Transformer. “In finance, we’re trying to use transformers to build and optimize models,” Mirla adds. “And things are evolving very quickly.”
How we’re using AI at Genus
As a quantitative manager, we’ve been using mathematical models at Genus for many years. “We’re a data-oriented company, and we started using AI algorithms five years before things got big,” Wachell says. “Machine learning is built into our macro models for asset allocation”.
“But Gen AI has opened a lot of doors in terms of where we can go from here.” Wachell says. “For example, say we like a stock, we can use Gen AI to break down its revenue segments and look at the things going on now, like trade tariffs, and see where its revenues are coming from to manage risks and make sense of the information. The marketplace is becoming more complex, and we’re looking at third-party AI models to help us understand themes.”
On the investment side, Gen AI can help to make sense of the volume of information required to make investment decisions. It can help us understand global relationships within the data we rely on to build our portfolios and actively manage them. In future, we plan to use Gen AI along with knowledge graphs to help us map out complex relationships.
We’re also planning to use Gen AI for middle office productivity purposes – particularly with our accounting and compliance teams. “On the investment side, nothing is going to replace humans,” Wachell says. “But AI makes humans more productive.”
And on the client side, AI can help to ensure that our clients’ personal investment philosophies and policies are actually aligned with their investments.
AI agents: The next big thing
As Gen AI continues to improve, the next wave of adoption will include AI agents – virtual coworkers that can complete complex tasks. At Genus we’re exploring adding AI agents as part of our compliance team, as well as agents that can review complex aspects of stock analysis to give us more insights. “ChatGPT is a general-purpose AI model with broad knowledge,” Mirla says. “For finance-specific applications, we’re fine-tuning our own AI to deliver precise, domain-aware insights.”
AI agents could also help us build better investment models. “Right now, we’re working to uncover new relationships within the data, and that research is time consuming,” Wachell says. “AI agents now have memories and tools, and you can give them a task and they’ll do it.” Of course, any model using AI requires extensive testing and privacy controls, and those are top of mind for us too.
Human-led investing is still essential
While AI continues to provide advanced tools to improve the work of investment analysts and portfolio managers, there’s still no replacement for human wealth managers. AI can help with data management, insights and productivity, but it still takes a human to understand the intricacies of nuance, empathy and the complexities of family dynamics in wealth planning. “We don’t think AI agents are a replacement for humans,” Wachell says.
Plus, it’s important to recognize that AI technologies are subject to inherent risks and limitations, and technology-assisted insights do not replace the need for personalized advice. That’s why all investment decisions at Genus are ultimately made with Portfolio Manager professional oversight and client-specific considerations.
But wealth managers will need to evolve their skill sets to effectively work with AI. “You can ask an AI agent to do things for you that you never could do before if you can craft the right prompts,” Wachell says. “Anybody can do it with domain knowledge and the ability to give directions.”
In fact, Mirla adds, those who are willing to experiment with AI will “give superpowers” to their existing tools and models. We’re seeing it happen in ESG investing especially – where AI agents can monitor how companies are progressing with their ESG goals. “This can happen in real time, providing more transparency and accountability,” Wachell says.
Interested in learning more about AI and how it’s being used in wealth management? Join our free webinar on April 17. Find out more and register here. (This webinar is for educational purposes only and does not constitute an offer or solicitation for investment advice.)
The information in this article is for informational purposes only and does not constitute investment, legal, or tax advice. Forward-looking statements are subject to risks and uncertainties. Past performance is not indicative of future results. Individual results may vary. Please consult with a qualified advisor before making investment decisions.
References:
Yee, L., Chui, M., & Roberts, R. (2024, July 24). Why agents are the next frontier of generative AI. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/why-agents-are-the-next-frontier-of-generative-ai
