How financial firms can make the most of AI
February 04, 2025
Artificial intelligence (AI) is creating a stir in every sector, and capital markets is no exception. But what opportunities does AI hold for one of your firm’s most precious assets: data? Whether you’re responsible for moving money, keeping it safe or putting it to work, applying AI to financial and customer data could be key to your competitive advantage. But you need to handle with care.
Why AI means business for data
AI presents a number of benefits for financial firms, and it’s good news for your data. Not only do AI tools potentially allow you to access and analyze more data faster, they may also help you incorporate it more effectively into your decision-making processes.
In other words, AI may allow you to make better use of existing systems and find new ways to operate more efficiently, streamline your processes, increase your insight and reduce costs and risks.
According to the 2024 FIS Global Innovation Research, a high proportion of companies around the world are already utilizing AI and machine learning tools to identify and manage operational risks. Cybersecurity and detection are the most common applications of AI and machine learning for operational risk management for more than 60% of business leaders, followed by risk modelling (over 50%).1
But new use cases are emerging all the time.
When AI is built into your treasury and risk management system, the ability to probe more data points at high speed helps you create more accurate cash flow forecasts, generate valuable, real-time insights and make more informed decisions about moving money and managing liquidity.
If you’re handling a complex collateralized loan obligation contract, which you’ve traditionally processed manually, AI can help you rapidly extract and validate all the data you need for straight-through processing in downstream systems. That means you may be able to onboard new customers and start servicing their deals more rapidly.
AI can also be highly efficient at spotting and predicting mistakes in data. In securities processing, firms historically have run any checks for breaks at the end of the trading day. Now, through constant real-time monitoring, AI can forecast when and where breaks are likely to happen. This way, you can act on and resolve them in advance.
More broadly, AI may help you receive near-instant answers from data rather than having to log into different systems, read through multiple spreadsheets or request information from colleagues. Again, AI is significantly improving efficiency, productivity and insight, and helping firms make the right decisions in excellent time.
AI advancements and adoption
AI advanced fast, especially since the advent of generative AI and its incorporation into personal computers, smartphones, email programs and collaboration tools. In 2025, that evolution is likely to continue apace. We’ll likely see more generative AI tools built into not only consumer technology, but also financial and business systems to improve the customer and employee experiences.
In capital markets, that means possibly making it easier for users to interact with and navigate their front-, middle- and back-office solutions, in some cases enabling them to converse with chatbots for the help and answers they need. As more firms start utilizing generative AI in their internal systems – whether to help them write emails, carry out research or manage their HR and finance functions – they will expect it from their third-party financial technology as well.
As time goes on, generative AI will enable capital markets firms to do even more. Today’s tools only scratch the surface of AI’s potential in terms of capabilities and speed. In actuarial and risk modeling, for example, generative AI is likely to take the production of results and analytics to a new level and further accelerate the pace of innovation.
When it comes to analyzing high volumes of data to identify patterns and simulate risks, generative AI’s power and efficiency are already unprecedented. For capital markets firms, generative AI may help access more data points than ever – on a national or global scale – for an improved understanding of where investments should be made. Moreover, AI can help firms establish better, more scientific practices for how they access their data and gain that understanding.
AI is likely to bring major advantages for providers of financial technology, too. It can help us understand a client firm’s ecosystem, analyze its requirements more quickly and considerably accelerate our software development, build and implementation processes. As a result, vendors and clients may be able to innovate faster.
Attitudes and obstacles to AI
Around the world, capital markets firms’ readiness to adopt AI varies from region to region. In parts of Asia, FIS® has seen clients make more aggressive use of AI tools, while organizations in certain European countries approach the implementation of AI more conservatively and with an eye on expanding its usage. In the U.S., there are opportunities, but there is also a certain hesitancy.
To some extent, research statistics reflect the different degrees of speed and comfort in adopting AI. For example, 70% of business leaders in Hong Kong use AI and machine learning for risk modeling compared to 54% in the U.K. and 45% in the U.S.1
0
% A key concern remains, understandably, about the data privacy and control over intellectual property (IP) that AI models do or don’t provide.
If you’re analyzing data with open AI models, you risk sharing at least some of your data and IP with the rest of the world. And for capital markets firms, particularly those that specialize in trading and investment, a big part of their competitive edge lies in keeping that data to themselves.
But that doesn’t mean these organizations shouldn’t use AI, more that they may want to adopt close-ended models that contain only their own data and sit safely behind their own firewalls.
Strategies for financial firms facing rising costs
From competition to regulation, inflation and outdated business models, firms are facing increasing costs on all sides. Andres Choussy, EVP, Group President, Trading and Asset Services, FIS, talks about strategies that can help you get ahead of these issues.
What’s your AI strategy?
Bad actors are already testing the limits of how far open AI models can protect your IP. In a year or two, there’s likely to be a large spike in legal claims that could create a whole new set of challenges for the capital markets industry and beyond.
In the meantime, though, it’s time to take advantage of the ever-growing skillset of AI in a way that makes most sense for your business. The key is to develop a more strategic approach to adopting the latest tools and technologies, particularly for managing operational and data risks.
Compared to the 63% of CEOs that claim to use AI and machine learning tools for cybersecurity and threat detection, only 21% have adopted them for data quality management and 37% for fraud protection.1
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% Given most firms’ extreme exposure to cyberrisks, it’s understandable that cybersecurity is a bigger focus of their investment in AI.
It’s important to take the long view and look past tactical uses of AI that combat only the most immediate risks. As a critical component of how your capital markets firm can operate more effectively and better manage its risks, you should consider making AI an integral part of a holistic IT strategy that helps meet your business objectives, innovate more quickly and make money work harder.
AI is a powerful technology and it needs to be managed carefully and integrated steadily and safely into capital markets operations.
1FIS, Global Innovation Research 2024 surveyed 2,000 firms across financial services and other industries in the U.S., U.K., Singapore, Hong Kong and Australia.
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