Artificial Intelligence (AI) and Machine Learning (ML) were recognized as the most influential technology for trading for the third year in a row, according to J.P. Morgan’s E-Trading Edit survey.
According to Patrick Whelan, head of FICC Digital Markets, AI and machine learning topped the list of technological advancements expected to impact the industry in 2025. “AI and machine learning came out on top once again,” he said, underscoring the pivotal role these technologies will play in automating processes, improving efficiencies, and driving innovation across the financial sector.

In the latest episode of Market Matters on J.P. Morgan’s Making Sense podcast, Patrick Whelan and Gergana Thiel, global co-head of Macro Sales, discussed this year’s survey findings.
The conversation centered around how technology, particularly advancements in AI, ML, and e-trading, is reshaping the trading landscape and influencing market participants’ strategies and predictions.
J.P. Morgan has been at the forefront of investing in these technologies, as Whelan noted. “We’ve already invested a lot in that space, and we continue to do so. We’re looking to leverage the data we have to drive more efficiencies and automate more of our processes,” he explained. While the firm continues to explore how to create commercial value from AI, the long-term potential for AI to revolutionize trading is clear. “I think we’re still learning about how we can create commercial value through the use of LLM and large-scale AI efforts, but by 2025, we’re going to see more of that coming into the day-to-day of both our sales traders and clients.”
Another key insight from the survey was the unanimous agreement among respondents that e-trading will continue to grow in 2025. “It was the only part of the survey where we had 100% agreement,” Whelan revealed. This signals a strong industry-wide shift toward digital platforms and highlights the need for firms to continue innovating in e-trading solutions.
As Whelan pointed out, areas such as emerging market rates and credit will experience significant electronification in the coming years. “They were pointing to EM rates and credit as being two of the areas that will continue to grow in terms of electronification in 2025,” he said. For J.P. Morgan, this represents a key investment focus, and the firm is committed to expanding its e-trading offering worldwide.
In line with growing demands for seamless digital trading experiences, J.P. Morgan is looking to develop new solutions that integrate technology more effectively with client needs. Whelan emphasized the importance of offering robust direct API solutions across asset classes. These solutions enable better connectivity with clients, making it easier for them to access liquidity and data in real-time.
“These types of solutions allow us to provide more competitive pricing, better analytics, and ultimately reduce information leakage,” Whelan explained. By continuing to enhance their single dealer platform, Execute, J.P. Morgan aims to help clients achieve more efficient trading while reducing costs. “Reducing brokerage and execution costs is an important focus area for 2025,” he added, noting how technology will continue to play a critical role in meeting this objective.

A critical aspect of technological advancement discussed in the podcast was the increasing demand for real-time data and analytics. Thiel noted that clients are increasingly relying on instant access to large data sets to make informed trading decisions. “Real-time data and analytics has become central to the investment process and the way clients engage with the market,” she remarked.
Whelan agreed, emphasizing that J.P. Morgan is focused on delivering better analytics and more innovative solutions to meet client needs. “Working with large data sets and providing real-time data both to our sales and traders as well as to our clients is a continued focus,” he said. However, he also acknowledged the challenges in some asset classes that are still transitioning toward full electronification. “In certain asset classes, it’s much more highly developed, but in others, we have to come up with more innovative analytics that clients are looking for,” he explained.
As the trading world moves toward more sophisticated data consumption methods, Whelan noted the need for both visual and API-based delivery systems to ensure that clients have access to the information they need when they need it.
As the conversation drew to a close, Whelan and Thiel shared their predictions for 2025 in three words. Whelan’s key terms were “data, connectivity, and growth.” “Data” reflects its crucial role in decision-making, while “connectivity” highlights the firm’s commitment to enhancing digital platforms and fostering better connections between clients and markets. “Growth” encapsulates the ongoing expansion of e-trading solutions across various asset classes.
Thiel, on the other hand, chose “tariffs, technology, and the U.S. dollar.” She highlighted how tariffs will continue to shape the global market outlook, while technology remains at the core of the trading transformation. Thiel also pointed to the U.S. dollar’s significance in signaling market sentiment.
AI is Now a Need, Not a Want for Meeting SEC Regulatory Reporting Requirements
By Laurent Louvrier, VP of Product, Artificial Intelligence at Confluence
The SEC’s introduction of new regulations over the last few years, including the Shareholder Reporting Rule, has meant increased reporting obligations for firms without a corresponding increase in the time, money, and human capital required to meet these obligations.
Firms are under pressure to have the right data and systems in place, leading to rising costs and resources at a time when many firms are also grappling with an overwhelming volume of unstructured data. To source, structure, arrange and report on this data is a significant undertaking given the amount of data teams need to parse. It’s becoming unfeasible for firms to tackle this using their traditional technologies.
New technologies like Gen AI and LLMs allow the automation of manual tasks like checking, reconciling, translating, and reasoning about information that cannot be easily handled by traditional technologies due to the nature of unstructured data. To use the SEC’s Tailored Shareholder Report requirements as an example, manual reconciliation to meet this requirement on or after July 24 may take a typical manager hundreds of business days to reconcile the reports each time. Firms must do this within a 60-day window, which becomes operationally impossible.
Advanced AI models are now capable of mining and pinpointing discrepancies in language and numeric information between financial reports and the TSR, automatically parsing data from third-party financial reports, and interrogating and reconciling it within seconds leading to a 90% increase in efficiency. This directly addresses the key challenges our clients face with data reconciliation and compliance validation under tight deadlines. These solutions convert complex unstructured data into valuable insights, reducing what would typically take weeks of manual work into a streamlined process that delivers significant efficiency and cost savings. While there’s still human oversight needed in all of this, AI can radically improve the efficiency of information and dissemination in financial reporting and compliance.
So, what’s holding firms back from using AI? Largely it has been resistance to change and the perceived risks regarding data privacy and security. To ease any concerns, I would first say companies should examine their business processes to evaluate where they allocate their time and resources and how AI can transform these areas to gain efficiency. A better understanding of business priorities and processes will help make decisions on where to start to leverage AI. While some organizations may have the capability to develop AI solutions internally, it often makes more sense to partner with established vendors who have the business process and AI expertise that can bring proven immediate business value.
As new regulations with more arduous reporting requirements arise, demanding greater time and resources, it will be especially important for firms to replace tedious manual validation processes and embrace productivity-enhancing technologies or risk being left behind. By leveraging AI, investment compliance operations can streamline SEC reporting processes, enhance compliance, and mitigate regulatory risks more effectively.