The financial industry is undergoing a major transformation, led by the adoption of agentic workflows—AI-driven systems designed to enhance efficiency, precision, and decision-making in global markets, according to Emily Prince, London Stock Exchange Group’s AI expert & Group Head of Analytics.
“Workflows aren’t just changing the industry—they’re redefining how we operate,” said Prince on the sidelines of FIA Boca.
“For example, if you’re a risk manager, assisted workflows mean you no longer have to manually compile reports. AI can gather the necessary data, organize it, and even generate insights—freeing up time for more strategic decision-making.”
Beyond productivity, these AI-driven workflows reduce human error. “Financial processes often involve numerous manual steps across multiple teams, which increases the risk of mistakes. By automating and streamlining these tasks, we not only improve efficiency but also significantly enhance accuracy,” she explained.
However, Prince cautions against seeing AI as a “magic bullet.” “The real benefits come when organizations have a clear understanding of their workflows and where AI can add value. Simply adding AI to a process won’t automatically make it 20% more efficient—you need to have a clear problem statement and success criteria.”
At LSEG, AI is driving innovation both internally and externally. Internally, AI enhances customer support by delivering real-time, context-aware insights.
“We’re using AI to provide timely, cognitive answers to customers by pulling the best information quickly,” Prince told Traders Magazine. “Additionally, AI-driven code generation is improving efficiency in software development,” she said.
Externally, AI is transforming financial products. “One of the most exciting advancements is actioning natural language into a series of financial tasks – from the generation of scenarios to pricing and risk analysis for portfolios of securities,” she explained. “Previously, tasks that took weeks and required multiple teams can now be completed in seconds. By integrating deep financial expertise with AI, we’re making these processes faster and more precise.”
Prince highlighted LSEG’s Visual Studio Code product, which enables clients to run complex financial models across vast datasets. “This tool allows for scenario analysis and risk assessment at an unprecedented speed—empowering users to make better decisions, faster.”
AI models are improving in their ability to analyze market dynamics, but their predictive power remains complex. “One major advantage AI has over humans is its ability to process massive amounts of data continuously,” Prince explained.
“Humans have limited context windows, and external factors like breaks or cognitive / environmental biases impact their ability to consume large volumes of data in real time – and then make the ‘best’ possible decision. AI, on the other hand, can detect patterns across much broader datasets which may have otherwise been undetected.”
However, regulatory challenges arise when AI is involved in high precision decision-making. “Regulators expect financial institutions to provide clear, explainable justifications for mission critical decisions,” said Prince. “While AI can support these decisions, it’s crucial to ensure that final outputs remain interpretable and compliant with regulatory standards.”
The rise of AI-driven financial decision-making presents challenges for regulators. “Regulators are balancing innovation with consumer protection,” said Prince. “Rather than imposing rigid rules, we see an increasing focus on risk-based frameworks that support AI adoption while ensuring transparency and accountability.”
One of the biggest hurdles is the lack of global regulatory alignment. “If financial institutions operate under different AI regulations in the US, Europe, and the UK, it complicates product development and compliance,” she explained. “The more policymakers can converge on global AI principles, the better we can foster both innovation and consumer protection.”
While AI and cloud technology are driving change, Prince believes the real shift is cultural. “The biggest innovation isn’t just AI itself—it’s the willingness of financial institutions to challenge their traditional business models,” she said.
“Financial firms are now asking, What does this mean for our business? They are interrogating long-standing processes and looking for ways to leap forward. Previously, optimization happened in small steps—moving from B to C to D. Now, AI allows us to jump from A to M, fundamentally rethinking how financial services operate.”
For Prince, the ultimate goal is empowering financial professionals with the right information at the right time. “AI isn’t about replacing human decision-making—it’s about making it smarter, faster, and more informed,” she concluded.
