Technology has long been a key component of any operations strategy in capital markets. But the recent rise of AI and other solutions is changing the operations landscape at a rapid pace. SmartBrief recently caught up with Ryan Clare, Global Head of Capital Markets – Go To Market at ServiceNow, to learn more about how technology is powering today’s markets and how AI is being leveraged to help operations evolve today – and in the future.
(Note: This interview has been edited for length and clarity)
How do you see AI making an impact in areas like market infrastructure, trading, risk management and compliance?
With regard to market infrastructure, it is trade surveillance and the ability to process enormous amounts of data and get real insights. It’s always been a challenge for most systems to be able to process the data and then to actually get information out of it. You have to run a model for hours before you could actually see the results. Being able to see what’s going on in the market at much faster speeds is really powerful. AI is also going to help with back office functions by helping firms make smarter clearing and settlement decisions.
For trading, the most visible impact of AI is with algo trading and the ability to do high-frequency trading strategies powered by AI, rather than waterfall logic. The most interesting part of it is that those models are going to be able to adapt as market conditions change. They’re going to be able to consume non-structured, unstandardized data and flex the model. A good example is the model will be able to scan negative news or articles being published in real-time, digest that data and change the strategy or make decisions based on that new information.
In risk management, AI is able to do exposure tracking and take in real market events and run them through an algorithm to see if they’re correct. When you’re doing risk management, you’re usually looking back historically. Typically, you download that data, crunch it and try to analyze it, but it’s often too late by that point to make risk management decisions. But with AI, that process is faster and you can look into playing Monte Carlo events in real-time scenarios. Eventually, risk management is going to require AI to police AI.
When I think about where AI is going to change compliance, I go back to trade surveillance. You only have a small amount of time to make sure you report your trades and monitor them correctly and demonstrate that to the regulator, but also there’s a Herculean lift that goes into KYC and AML checks. I think that’s where AI is really going to help within compliance. You could have AI agents across the full life cycle of those processes. AI is going to be able to take a lot of that compliance burden away so firms can focus on complex analysis to make sure that they’re meeting regulatory requirements.
How is increased automation changing the operations landscape at financial services firms?
For starters, automation has increased the job satisfaction and the quality of life for operations staff. It’s allowing them to focus on the high-value tasks and become more involved in change-type work. It’s also changing the way people actually operate in the operations department and reducing the gap between people and technology.
Automation is no longer about cost saving. It’s about scalability, resiliency and flexibility. From traditional RPA to agentic AI, we’ve got to integrate fully across all types of processes and workflows. People should be more worried about the high-value exceptions. We should be able to increase straight-through processing and generally provide a better quality of life for the employees, but also create a better client experience. Operations is going to become an added value department that helps firms gain market traction and provide better services to their clients.
What are some of the biggest compliance concerns that firms have about the deployment of AI?
The biggest concern is that it’s a black box technology. How do you actually validate that the model is doing what it should be and the inputs are correct and it’s being governed and controlled? You’ve got to have really strong data governance to make sure that the inputs and the prompts going into your model aren’t incorrect or being abused. If people are making decisions, especially trading decisions, off the back of the output from the AI, that’s where compliance is going to have to be strong. It goes back to what I said earlier about policing AI with AI. I think there’s going to be a new market for compliance-type technology, where you can use AI to validate your AI.
Aside from AI, what other technologies should we be keeping an eye on when it comes to capital markets?
The amount of processing power that cloud infrastructure is going to need is ginormous. As we’re requiring this real-time processing to happen, the computing power behind it and the cloud infrastructure is going to have to keep pace. The more we give AI to the individual consumer, whether that’s through an organization or as an individual, the energy that’s going to be required to run those models is going to need to be reviewed and expanded.
Another important area to watch revolves around privacy-preserving technologies like federated learning. One challenge we’ve always had with AI is knowing how much to put into the model. Most firms don’t allow you to put proprietary information into models, so unless you have it encapsulated in your organization, you can’t really get the full benefit of the data. As federated learning comes in, where you can just listen to the activities and not have to consume that data, you can have enough information around it to be able to process it and enrich the model. That’s going to become very important, especially where you have rules around sharing sensitive information. Federated learning will open up a whole new world of AI because you’ll be able to reuse the AI’s large language models as an organization, from customer to customer, without sacrificing privacy or proprietary data.
Do you have any bold predictions for what we will be talking about when it comes to technology and capital markets in 2-3 years?
I’m waiting for the first firm to come out with 24/7 trading desks. I’m talking about an offering that is automated through agentic AI and enables the user to hedge or potentially even negotiate pricing. Obviously, it would have to be for some of the more simplistic products. It might be a bit of a stretch to get approved by some of the regulatory bodies, but I think it would be really exciting to have a fully automated trading desk, from front to back.
What steps should business leaders in capital markets be taking right now to prepare their operations for the future?
Coming from a transformation background, I’ve got two points. The first one is data, data, more data. You’ve got to have good, high quality data for any of the technologies we’ve been talking about to work. If it’s garbage in, then it will be garbage out. Your data has to be governed and the reference data has to be supported around it. That’s where you’re going to create new roles, like data stewards, who are really going to have to be on their game to make sure the inputs and components that are being used are correct. Otherwise, you’re just going to create data outputs that no one can use, and that’s never going to produce the results and ROI you are seeking.
The second thing is enterprise innovation. I touched on it earlier, with the operational people becoming closer to technology. I think the days of technology driving an initiative on its own are over. It’s now a joint effort. Everyone, from senior leaders on down to entry-level staff, needs to be engaged and play a role in innovation. Some of the young individuals coming into the market are already skilled in writing Python, or have potentially even written some of the AI models themselves. There are a lot of quality thoughts and requirements that can be gathered from them, so they should be empowered to help the enterprise innovate.