Reed Smith partners Claude Brown and Romin Dabir discuss the challenges and opportunities of artificial intelligence in the financial services sector. They cover the regulatory, liability, competition and operational risks of using AI, as well as the potential benefits for compliance, customer service and financial inclusion. They also explore the strategic decisions firms need to make regarding the development and deployment of AI, and the role of regulators play in supervising and embracing AI.

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Transcript:

Intro: Hello, and welcome to Tech Law Talks, a podcast brought to you by Reed Smith's Emerging Technologies Group. In each episode of this podcast, we will discuss cutting edge issues on technology, data, and the law. We will provide practical observations on a wide variety of technology and data topics to give you quick and actionable tips to address the issues you are dealing with every day. 

Claude: Welcome to Tech Law Talks and our new series on artificial intelligence, or AI. Over the coming months, we'll explore key challenges and opportunities within the rapidly evolving AI landscape. Today, we're going to focus on AI in financial services. And to do that, I'm here. My name is Claude Brown. I'm a partner in Reed Smith in London in the Financial Industry Group. And I'm joined by my colleague, Romin Dabir, who's a financial services partner, also based in London. 

Romin: Thank you, Claude. Good to be with everyone. 

Claude: I mean, I suppose, Romin, one of the things that strikes me about AI and financial services is it's already here. It's not something that's coming in. It's been established for a while. We may not have called it AI, but many aspects it is. And perhaps it might be helpful to sort of just review where we're seeing AI already within the financial services sector. 

Romin: Yeah, absolutely. No, you're completely right, Claude. Firms have been using AI or machine learning or some form of automation in their processes for quite a while, as you rightly say. And this has been mainly driven by searches for efficiency, cost savings, as I'm sure the audience would appreciate. There have been pressures on margins and financial services for some time. So firms have really sought to make their processes, particularly those that are repetitive and high volume, as efficient as possible. And parts of their business, which AI has already impacted, include things like KYC, AML checks, back office operations. All of those things are already having AI applied to them. 

Claude: Right. I mean, some of these things sound like a good thing. I mean, improving customer services, being more efficient in the know-your-customer, anti-money laundering, KYC, AML areas. I suppose robo-advice, as it's called sometimes, or sort of asset management, portfolio management advice, might be an area where one might worry. But I mean, the general impression I have is that the regulators are very focused on AI. And generally, when one reads the press, you see it being more the issues relating to AI rather than the benefits. I mean, I'm sure the regulators do recognize the benefits, but they're always saying, be aware, be careful, we want to understand better. Why do you think that is? Why do you think there's areas of concern, given the good that could come out of AI? 

Romin: No, that's a good question. I think regulators feel a little bit nervous when confronted by AI because obviously it's novel, it's something new, well, relatively new that they are still trying to understand fully and get their arms around. And there are issues that arise where AI is applied to new areas. So, for example, you give the example of robo-advice or portfolio management. Now, these were activities that traditionally have been undertaken by people. And when advice or investment decisions are made by people, it's much easier for regulators to understand and to hold somebody accountable for that. But when AI is involved, responsibility sometimes becomes a little bit murkier and a little bit more diffuse. So, for example, you might have a regulated firm that is using software or AI that has been developed by a specialist software developer. And that software is able to effectively operate with minimal human intervention, which is really one of the main drivers behind it, behind the adoption of AI, because obviously it costs less, it is less resource intensive in terms of skilled people to operate it. It but under those circumstances who has the regulatory responsibility there is it the software provider who makes the algorithm programs the software etc etc and then the software goes off and makes decisions or provides the advice or is it the firm who's actually running the software on its systems when it hasn't actually developed that software? So there are some naughty problems i think that regulators are are still mulling through and working out what they think the right answers should be. 

Claude: Yeah I can see that because I suppose historically the the classic model certainly in the UK has been the regulator say if you want to outsource something thing. You, the regulated entity, be you a broker or asset manager or a bank, you are, or an investment firm, you are the authorized entity, you're responsible for your outsourcer or your outsource provider. But I can see with AI, that must get a harder question to determine, you know, because say your example, if the AI is performing some sort of advisory service, you know, has the perimeter gone beyond the historically regulated entity and does it then start to impact on the software provider. That's sort of one point and you know how do you allocate that responsibility you know that strict bright line you want to give it to a third party provider it's your responsibility. How do you allocate that responsibility between the two entities even outside the regulator's oversight, there's got to be an allocation of liability and responsibility. 

Romin: Absolutely. And as you say, with traditional outsource services, it's relatively easy for the firm to oversee the activities of the outsource services provider. It can get MI, it can have systems and controls, it can randomly check on how the outsource provider is conducting the services. But with something that's quite black box, like some algorithm, trading algorithm for portfolio management, for example, it's much harder for the firm to demonstrate that oversight. It may not have the internal resources. How does it really go about doing that? So I think these questions become more difficult. And I suppose the other thing that makes it more difficult with AI to the traditional outsourcing model, even the black box algorithms, is by and large they're static. You know, whatever it does, it keeps on doing. It doesn't evolve by its own processes, which AI does. So it doesn't matter really whether it's outsourced or it's in-house to the regulated entity. That thing's sort of changing all the time and supervising it is a dynamic process and the speed at which it learns which is in part driven by its usage means that the dynamics of its oversight must be able to respond to the speed of it evolving. 

Romin: Absolutely and and you're right to highlight all of the sort of liability issues that arise, not just simply vis-a-vis sort of liabilities to the regulator for performing the services in compliance with the regulatory duties, but also to clients themselves. Because if the algo goes haywire and suddenly, you know, loses customers loads of money or starts making trades that were not within the investment mandate provided by the client where does the buck stop is that with the firm is that with the person who who provided the software it's it's all you know a little difficult. 

Claude: I suppose the other issue is at the moment there's a limited number of outsourced providers and. One might reasonably expect competition being what it is for that to proliferate over time but until it does I would imagine there's a sort of competition issue a not only a competition issue in one system gaining a monopoly but that particular form of large model learning then starts to dominate and produce, for want of a better phrase, a groupthink. And I suppose one of the things that puzzles me is, is there a possibility that you get a systemic risk by the alignment of the thinking of various financial institutions using the same or a similar system of AI processes, which then start to produce a common result? And then possibly producing a common misconception, which introduces a sort of black swan event that was anticipated. 

Romin: And sort of self-reinforcing feedback loops. I mean, there was the story of the flash crash that occurred with all these algorithmic trading firms all of a sudden reacting to the same event and all placing sell orders at the same time, which created a market disturbance. That was a number of years ago now. You can imagine such effects as AI becomes more prevalent, potentially being even more severe in the future. 

Claude: Yeah, no, I think that's, again, an issue that regulators do worry about from time to time. 

Romin: And I think another point, as you say, is competition. Historically, asset managers have differentiated themselves on the basis of the quality of their portfolio managers and the returns that they deliver to clients, etc. But here in a world where we have a number of software providers, maybe one or two of which become really dominant, lots of firms are employing technology provided by these firms, differentiating becomes more difficult in those circumstances. 

Claude: Yeah and I guess to unpack that a little bit you know as y