Introduction

The asset management and wealth management sectors could see AI boost efficiency in the short term and drive alpha in the medium to long term.

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Mike Cyprys: Welcome to Thoughts on the Market. I'm Mike Cyprys, Morgan Stanley's Head of U.S. Brokers, Asset Managers and Exchanges Team. 

Bruce Hamilton: And I'm Bruce Hamilton, Head of European Asset Management and Diversified Financials Research. 

Mike Cyprys: And on this special episode of the podcast, we'll talk about what the Generative A.I Revolution might mean for asset and wealth managers. It's Tuesday, October 17th at 10 a.m. in New York. 

Bruce Hamilton: And 3 p.m. in London. 

Mike Cyprys: My colleagues and I believe that Generative A.I is a revolution rather than simply an evolution and one that is well underway. We think Gen A.I, which differs from traditional A.I in that it uses data to create new content, will fundamentally transform how we live and work. This is certainly the case for asset and wealth management, where leading firms have already started deploying it and extracting tangible benefits from Gen A.I across an array of use cases. Bruce, what has been the initial focus among firms that have successfully deployed Gen A.I so far? And, something that has been top of mind for most of us, is Gen A.I replacing human resources? 

Bruce Hamilton: So Mike, clearly it's early days, but from our conversations with more than 20 firms managing over $20 trillion in assets, it seems clear that the immediate opportunities are mainly around efficiency gains rather than top-line improvements. However over time, as these evolve, we expect that this can drive opportunity for top-line also. All firms we spoke with see the importance of humans in the loop given risks, so A.I as copilot and freeing up resource for more value added activities rather than replacing humans. 

Mike Cyprys: What are some of the top most priorities for firms already implementing Gen A.I? And in broad terms, how are they thinking about integrating Gen A.I within their business models? 

Bruce Hamilton: So opportunities are seen across the value chain in sales and client service, product development, investment in research and middle and back office. Initial efficiency use cases would include drafting customized pitch or RFP reports and sales, synthesis of research and extraction of data in research, and coding in I.T.. Now Mike, specifically within the asset management space, there are two primary ways Gen A.I is disrupting. One is through efficiencies and two revenue opportunities. Can you speak to the latter? How would Gen A.I change or improve asset management? And do you believe it will truly transform the industry? 

Mike Cyprys: Absolutely. I think it can transform the industry because what's going to change how we live, how we work, and that will have implications across business models and the competitive landscape. I believe we're now at a A.I tipping point, just in terms of its ability to be deployed on a widespread basis across asset managers. The initial focus is overwhelmingly on driving efficiency gains and at the moment there's skepticism if Gen A.I can drive product alpha, but it should help with some of the maintenance tax around collecting and summarizing information and cleaning data. This should help release PM's of time to focus more on higher value idea generation and testing their ideas, which should help performance generation. I don't think it hurts. All in, we think this could result in up to 30% productivity gains across the investment functions. 

Bruce Hamilton: We've talked about how Gen A.I affects asset management. Do you think it can transform how financial advisers do their job and what kind of productivity gains are you expecting to see? 

Mike Cyprys: Financial advisors stand to benefit the most from Gen A.I because it should help liberate advisors time spent on routine or administrative tasks and allow them to focus more of their time on building deeper connections with clients and allowing them to service more clients with the same resources. And so that's how you get the revenue opportunity, by serving more clients and more assets. It's more of a copilot or tool that enhances human capabilities as opposed to replacing the human advisor. So on the wealth side, we do see more of a revenue opportunity for Gen A.I than we do on the asset management side in the near-to-medium-term. Use cases include collecting client information and interactive ways and summarizing those insights as well as proposing the next best actions and drafting engagement plans and talking points. All in, Gen A.I should help drive productivity improvements between 30 to 40% in the wealth sleeve. 

Bruce Hamilton: So Mike, what's your outlook for the next 3 to 5 years when it comes to the impact of Gen A.I on asset management? 

Mike Cyprys: It's really an expense efficiency play in the near to medium term for asset managers. But as you look out over the next 3-to-5 years, we could see a situation where A.I is embedded in a broader range of activities, from product development to portfolio management and trading areas, including trade optimization strategies, as well as brainstorming new product ideas tailored to client needs. Now in terms of assessing firms that are best placed, our qualitative assessment considers four main areas. First, there's firm scale and resources to allocate to both profitability and balance sheet capacity. Secondly, we consider a firm's in-house data and technology resources to drive change. Thirdly, are firms’ access to proprietary datasets where it can leverage A.I capabilities. And finally, there's the strategic priority assigned to A.I. by management. 

Bruce Hamilton: But Mike, what are some of the risks and limitations of A.I technology when it comes to wealth management and specifically to financial advisors rather than to back office functions? 

Mike Cyprys: We see the risks falling into two categories. There's technological risks on one side that includes hallucinations that can result in poor decisions, as well as inability to trace underlying logic and the threat of cyber attack and fraud. Then on the other side, there's usage risks, which include data privacy, improperly trained models, as well as copyright concerns. We're seeing firms respond to these challenges by maintaining a ‘human in the loop’ approach to A.I. adoption. That is a human is involved in the decision making process such that A.I operates with human oversight and intervention. 

Mike Cyprys: Bruce, thanks so much for taking the time to talk. 

Bruce Hamilton: Great speaking with you, Mike. 

Mike Cyprys: And thanks for listening. If you enjoy Thoughts on the Market, please leave us a review on Apple Podcasts and share the podcast with a friend or calling today.