How AI in FinTech Is Moving From Hype to Production Reality
In this episode of FinTech Focus TV, hosted by Toby, we are joined by Kaja Verhoeven Zupanc, Head of Machine Learning at Duco, to explore one of the most important shifts happening in financial technology today: the move from AI experimentation to real, production-level deployment.
For years, AI in financial services has been discussed as a future opportunity. But as Kaja explains, the industry is now entering a new phase, one where AI is not just a concept or a slide deck, but a tangible, operational tool delivering measurable value across data, operations, and workflows.
This conversation cuts through the noise and focuses on what actually matters: how AI is being applied, where it is delivering impact, and what this means for the future of FinTech hiring, technology teams, and operational efficiency.
AI in Financial Services: From Academia to Real-World Impact
Kaja’s journey into AI reflects a broader trend we are seeing across FinTech talent markets. With a background in computer science and a PhD followed by time in academia, she transitioned into industry to solve real-world problems, something many AI professionals are now doing as demand accelerates.
This shift from theoretical knowledge to applied AI is critical. Financial institutions are no longer looking for purely academic expertise; they need professionals who can deploy AI in live environments, integrate it into workflows, and deliver outcomes that impact business performance.
At Duco, this is exactly what Kaja and her team are focused on. The company, a cloud-native, no-code reconciliation and data automation platform, is designed to help organisations manage complex data from multiple sources, normalise it, and ensure accuracy through advanced matching and exception handling.
What makes this particularly relevant for FinTech recruitment is the growing demand for hybrid talent. professionals who understand both the technical foundations of AI and the operational realities of financial services.
Data Automation and AI: Solving the Real Bottleneck in FinTech
One of the most powerful themes in this episode is the focus on data. In financial services, data is both the greatest asset and one of the biggest operational challenges.
Duco’s platform addresses this by transforming unstructured data, such as legal contracts and OTC confirmations, into structured, usable information.
This is where AI is delivering immediate value. Rather than simply analysing data, AI is now enabling organisations to process, clean, and reconcile it at scale. This reduces manual effort, increases accuracy, and allows teams to focus on higher-value work.
For employers, this creates a new set of hiring priorities. There is increasing demand for professionals in data engineering, AI implementation, and automation, particularly those who can work across complex financial datasets.
For candidates, it highlights a clear opportunity: those who can bridge the gap between data, AI, and financial operations will be among the most sought-after talent in the market.
Agentic AI in FinTech: Beyond Automation to Intelligent Workflows
A major focus of the discussion is agentic AI, the next evolution of artificial intelligence that is reshaping how work gets done in financial services.
Unlike traditional automation, which focuses on predefined tasks, agentic AI introduces systems that can act as co-pilots, proactively supporting users and executing workflows.
At Duco, this evolution is already underway. Kaja explains how their AI journey has progressed from foundational capabilities to more advanced, agent-driven solutions that assist users in interacting with the platform, building processes, and managing data.
This is a significant shift. Instead of users manually navigating systems, AI agents can now interpret natural language instructions, configure processes, and optimise workflows.
For FinTech organisations, this represents a step change in productivity. For recruitment, it signals a shift in the skills required—moving from manual operational roles towards more strategic, AI-enabled positions.
Human-in-the-Loop AI: Building Trust in Regulated Environments
One of the biggest barriers to AI adoption in financial services has always been trust. In a highly regulated industry, accuracy, transparency, and accountability are non-negotiable.
Kaja highlights three key principles that underpin Duco’s approach to AI: keeping humans in the loop, ensuring explainability, and maintaining full auditability.
This is crucial. AI is not replacing humans; it is augmenting them. Organisations can decide where to trust AI and where to retain manual oversight, creating a flexible framework that balances efficiency with control.
For hiring managers, this reinforces the importance of governance-focused roles, including AI risk specialists, compliance professionals, and data governance experts.
For candidates, it demonstrates that the future of AI in FinTech is not just about building models, it is about understanding how those models operate within regulated systems.
AI Adoption in FinTech: From Slides to Production
A standout insight from this episode is the gap between AI hype and reality. Many organisations are still discussing AI in theory, but relatively few have deployed fully operational solutions.
Duco, however, is already delivering AI in production. Kaja notes that when clients see live demonstrations rather than presentations, their perception changes immediately.
This distinction is critical. The FinTech market is moving beyond experimentation and into execution.
For recruitment businesses like Harrington Starr, this creates a clear opportunity. Clients are no longer looking for AI strategists alone—they need professionals who can implement, scale, and maintain AI systems in live environments.
This is where the market is heading, and those who can deliver production-ready AI will define the next phase of FinTech innovation.
AI Productivity in Financial Services: Reducing Time by 90%
At the core of Duco’s AI strategy is a simple but powerful goal: reducing the time spent on data-related work by up to 90%.
This is achieved by identifying where clients spend the most time and prioritising those areas for automation.
One example discussed is exception handling. Traditionally, financial operations teams spend hours each day reviewing and resolving exceptions. With AI, this process can be transformed.
Instead of manually identifying issues, proactive agents can detect and prioritise them, suggest actions, and streamline resolution workflows.
This has profound implications for both productivity and talent. As repetitive tasks are automated, the role of the workforce shifts towards more analytical, strategic, and decision-making responsibilities.
The Evolution of AI Teams in FinTech Hiring
Another key theme is the changing structure of AI teams. Kaja highlights the importance of combining different skill sets, including traditional machine learning expertise, generative AI knowledge, software engineering, and ML operations.
This reflects a broader trend in FinTech recruitment. There is no longer a single “AI expert” profile. Instead, organisations need diverse teams that can collaborate across disciplines.
For employers, this means rethinking hiring strategies. Building effective AI teams requires a mix of experience levels and specialisations.
For candidates, it highlights the importance of continuous learning. The most successful professionals will be those who can adapt to new technologies while maintaining a strong foundation in core principles.
Agentic AI Adoption: Rapid Growth and Market Demand
The speed of AI adoption at Duco is striking. Within a short period, thousands of AI-driven rules were created and deployed, with a significant proportion of clients actively using the technology.
This demonstrates a key shift in the market. Once AI proves its value, adoption accelerates rapidly.
For FinTech leaders, the message is clear: waiting on the sidelines is no longer an option.
For recruitment, this creates urgency. As more organisations adopt AI, competition for skilled talent will intensify, particularly in areas such as AI engineering, data science, and platform development.
The Future of Agentic AI in Financial Technology
Looking ahead, the conversation turns to the future of agentic AI.
Kaja predicts a move towards orchestrated systems, where multiple AI agents communicate with each other to manage entire workflows.
In this model, different agents handle specific tasks, from data ingestion to reconciliation and exception resolution, working together seamlessly.
This represents a fundamental shift in how financial systems operate. Instead of isolated tools, organisations will have interconnected AI ecosystems that drive end-to-end processes.
For the workforce, this will require new skills in system design, AI integration, and cross-platform collaboration.
AI Trust and Transparency: The Key to Long-Term Adoption
As AI continues to evolve, trust will remain a central theme.
Kaja emphasises the importance of transparency and education in building confidence among users.
As people better understand how AI works and see its benefits in practice, trust will increase. This will enable organisations to gradually expand the role of AI within their operations.
For leaders, this highlights the need for clear communication and training. For recruitment, it underscores the value of professionals who can bridge the gap between technical and business stakeholders.
What This Means for FinTech Recruitment in 2026
This episode of FinTech Focus TV offers a clear message: AI is no longer a future concept, it is a present reality.
For Harrington Starr, and for the wider FinTech recruitment market, this creates both challenges and opportunities.
Demand for AI talent will continue to grow, particularly for roles that combine technical expertise with industry knowledge.
At the same time, the nature of work is changing. As AI takes on more operational tasks, organisations will need professionals who can manage, optimise, and govern these systems.
This is where strategic recruitment becomes critical. Businesses need partners who understand not just the technology, but the talent required to deliver it.
Why FinTech Leaders Need to Act Now
The final takeaway from this conversation is urgency.
AI is moving fast, and the gap between leaders and laggards is widening. Organisations that invest in AI now, and build the right teams to support it, will be best positioned to succeed.
For those still relying on manual processes or early-stage experimentation, the time to act is now.
As Kaja highlights, the future of AI in financial services is not about replacing people, it is about empowering them to do more valuable, impactful work.
And for FinTech leaders, that is where the real opportunity lies.
