Making AI part of our DNA
19 November, 2025
How AI Is Shaping the Future of Quant Research
The latest episode of FinTech Focus TV, recorded live at Quant Strats 2025, offers a thought-provoking and deeply grounded discussion with Federico Fontana, Chief Technology Officer at XAI Asset Management, hosted by Lucia. As one of the standout conversations at the event, this episode delves into the evolving intersection of AI, machine learning, data, and quantitative finance. Through Federico’s reflections, the conversation highlights the realities of technological change in systematic investing, and what it all means for the future of quant teams, trading infrastructure, research functions, and the broader FinTech ecosystem.
For audiences across the FinTech recruitment space, including firms looking to hire world-class quantitative researchers, data engineers, portfolio developers, and technology leaders, this episode provides a window into how some of the industry’s most advanced firms are thinking about innovation. Federico’s perspective is both pragmatic and ambitious, rooted in hands-on research, production-level infrastructure, and a clear understanding of the incremental yet significant movements shaping alpha generation and systematic asset management.
Inside the Role of a Modern CTO in Quantitative Finance
At the start of the episode, Lucia asks Federico to introduce himself and share the journey that led him to becoming CTO of XAI Asset Management. Federico explains that he has been part of XAI since day one, initially focusing on research and development efforts tied to the firm’s systematic strategy. Over time, as the business evolved, so did his responsibilities. He now oversees all aspects of data and trading infrastructure, guiding critical decisions around data coverage, engineering architecture, and the robustness of execution systems.
What emerges is a portrait of a CTO deeply embedded in both the research and engineering sides of the business, one who understands quantitative modelling as much as the technological ecosystems needed to support it. For organisations recruiting in the FinTech and quant sectors, Federico’s role exemplifies the hybrid leadership profile increasingly required across the industry: someone who can connect strategic research initiatives with operational technology delivery, ensuring the reliability and performance of systems core to alpha generation.
In describing his day-to-day work, Federico emphasises the continuous improvement of the trading infrastructure and expanding the breadth of data available to the firm’s models. His mention of robustness, coverage, and ongoing R&D reflects the reality of quant teams today. Performance is no longer achieved solely through breakthroughs in modelling but through a holistic alignment between data quality, systems engineering, and thoughtful research pipelines.
AI in Quant Finance: A Useful Tool, Not a Magic Solution
The heart of the conversation turns to the role of AI in modern quantitative research. Lucia references the panel Federico had spoken on earlier that day, centred on whether AI represents an extension of quant techniques or a genuine disruption. She asks how AI is influencing his team’s daily work.
Federico’s answer is measured and pragmatic. He explains that at XAI, AI and machine learning are viewed as useful tools, powerful, certainly, but not universally applicable. He stresses that the team is agnostic about which tools are best suited to which problems. Their goal is not to force machine learning into unnecessary places, but rather to identify where such techniques naturally add value.
Interestingly, he notes that AI thus far has been most beneficial in processing input data rather than in portfolio construction or risk management. This distinction reflects a trend many FinTech recruitment clients are seeing across the quant landscape: while machine learning has made strides in feature extraction, signal enhancement, and data structuring, traditional quantitative methods still dominate many core investment functions. For candidates entering the quant job market, and for employers shaping their hiring strategies, this reinforces the need for balanced, multi-disciplinary teams where data engineering, classical modelling, and applied machine learning coexist.
Is AI an Incremental Evolution or a Disruptive Shift in Systematic Investing?
When pressed on whether AI represents a natural evolution or a disruptive technology, Federico is clear: he sees it as an incremental improvement rather than a zero-to-one shift. To some observers, the arrival of new AI-driven products may appear sudden, as though they emerged from nowhere. But to those at the forefront of research, these developments are simply the latest steps in a long series of ongoing enhancements.
Federico points out that even with the excitement surrounding AI in financial technology, the changes happening are not radical breaks from the past. Instead, they are extensions of pre-existing methodologies. This mirrors a reality widely recognised in the FinTech recruitment market: firms driving genuine innovation are those with robust, longstanding research cultures, not those jumping onto trends in pursuit of shortcuts.
Lucia notes that AI’s hype has often overshadowed its practical limitations in finance, and Federico agrees. He points out that while AI may appear revolutionary from the outside, quant researchers view it within the broader continuum of evolving best practices.
For hiring managers, this suggests that the most successful quant teams will be those who blend curiosity with discipline, teams that innovate meaningfully without discarding fundamental principles of modelling, research, and data validation.
Testing Reinforcement Learning and the Future of End-to-End Portfolio Construction
Lucia moves the conversation into more specific territory, asking whether AI is contributing meaningfully to alpha generation. Federico acknowledges that in certain cases, it can. For example, XAI has explored using reinforcement learning to make aspects of portfolio construction more end-to-end. He explains that internal projects in this area have shown some positive results.
This is one of the few moments in the conversation where Federico points directly to potential future advancements in quant research. Reinforcement learning, in particular, has been a topic of excitement across FinTech, hedge funds, and systematic trading. However, its practical adoption remains limited, largely due to issues of interpretability, stability, and real-world constraints.
The fact that XAI has seen promise suggests that some firms are beginning to experiment at deeper levels of the investment lifecycle. For FinTech recruitment audiences, this reinforces the growing demand for talent comfortable with a fusion of machine learning, mathematical modelling, and high-quality engineering. Candidates who can collaborate across these domains, from data collection to execution, will be among the most sought-after profiles.
Why Better Data May Be More Transformational Than AI
One of the most insightful sections of the conversation comes when Lucia asks whether recent improvements in quant research are being driven more by improved data than by AI itself. Federico responds that a major positive change in recent years has been the structuring of previously unstructured data. Data providers are consolidating datasets more effectively, making it easier for research teams to evaluate data sources, test ideas, and accelerate the R&D cycle.
This theme is critical. While AI dominates headlines, the largest performance gains in systematic finance often come from advances in data engineering, data accessibility, and dataset consolidation. Structured, high-quality data enables faster experimentation, better modelling practices, and more confident decision-making. For FinTech recruitment clients building data teams, this underscores the importance of recruiting candidates with strong data engineering and data science skills, professionals who understand both the statistical and infrastructural challenges associated with large-scale datasets.
Federico explains that these improvements in data structure ultimately allow firms like XAI to make better use of the information available, shortening the distance between research questions and actionable insights.
Incremental Change vs. Industry Disruption: What Truly Moves the Needle?
When asked what distinguishes incremental improvement from true disruption, Federico reiterates that he sees few examples of overnight transformation in finance or technology. Even with the popularity of large language models, the real breakthroughs come from continuous adoption, testing, and refinement, not sudden leaps.
This perspective is highly relevant for firms thinking about long-term hiring strategies. Disruptive innovation often captures market attention, but sustained competitive advantage relies on team capability, culture, learning, and adaptability. The quant teams best positioned for the future are those that continue experimenting with new tools while grounding their work in rigorously validated methodologies.
Key Themes for the FinTech and Quant Community
As the episode draws to a close, Lucia asks Federico to share three key themes he hopes listeners will take away from the conversation. His reflections reveal three philosophical pillars underpinning strong, innovative quantitative research teams.
First, he emphasises the importance of observing what open-source communities are offering. He believes that many researchers unnecessarily reinvent the wheel when solutions already exist in the open-source ecosystem. Using and contributing to open source accelerates research, reduces waste, and connects teams to global innovation networks. For FinTech companies and hiring managers, this is a reminder to prioritise talent that is open-minded, collaborative, and aware of global tooling trends.
Second, Federico highlights the continued importance of academic research. Despite the rapid growth of AI, academic frameworks remain foundational. AI does not replace academic discipline; it enhances it. This insight is valuable for both recruitment strategies and career development in FinTech. The most successful quant candidates will be those who blend academic foundations with practical engineering and modern machine learning capabilities.
Lastly, he underscores the importance of a user-centric perspective when applying emerging technologies. Whether building tools for researchers, traders, or clients, the guiding principle should always be who ultimately benefits from the outcome. Technology, he says, is a tool, not an end in itself. In finance, this principle remains essential.
Why This Episode Matters for the Future of FinTech Talent and Innovation
For Harrington Starr’s community of FinTech professionals, hiring managers, and candidates, this episode of FinTech Focus TV offers a realistic and deeply informed view of the future of quantitative finance. Federico’s insights reflect a sector marked by constant evolution, where incremental change compounds into meaningful long-term progress.
It’s clear from this conversation that the future of quant research and machine learning in finance will not be dominated by sensational breakthroughs, but by teams that understand research discipline, technological pragmatism, data quality, and thoughtful innovation. Firms investing in the right talent today, from data engineers to quant researchers to technology leaders, will be the ones best positioned to thrive in this environment.
As quant, AI, and data-driven investing continue to shape the next era of FinTech, conversations like this one remain essential. And as always, Harrington Starr continues to champion the people, skills, and thinking that drive the industry forward.
