The financial technology industry is experiencing a significant transformation in its foundational structure.
It is transitioning from using passive AI tools that merely gather information to embracing “agentic AI” – systems that can think, make decisions, and act autonomously.
As these intelligent agents become more integrated into the financial landscape, the focus is moving beyond mere efficiency to a comprehensive overhaul of trust, governance, and accountability.
1. Re-wiring trust at the protocol layer
The most significant transformations are occurring at the intersection of financial management and physical logistics.
Gracie Page-Fozzati, a Partner at The Building Blocks, emphasises that we need to shift our perspective on digitisation; it shouldn’t be seen just as a means to boost efficiency.
She highlights the shipping industry as a key example, noting that a bill of lading serves as both a financial instrument and a “trigger for payment.”
“Digitising it isn’t about efficiency. It’s about re-wiring trust,” Fozzati explains. “What’s new now is that governance no longer sits in post-hoc reconciliation. It’s encoded directly into the process itself. The financial system stops reacting and starts enforcing.”
This shift allows for the automation of funds upon verified custody transfer and dynamic insurance coverage. Fozzati describes this evolution as “agentic finance in practice,” where the protocol layer rebuilds financial governance.
2. The era of the digital co-worker
As governance is increasingly integrated into systems, AI’s function is transforming.
Niraj Kumar, CTO of Onix, notes that the industry is shifting from using AI as mere passive assistants to making it an active contributor capable of taking action on its own.
“The future of agentic AI in fintech will shift from passive copilots to active Digital Co-workers that execute complex, multi-step workflows autonomously,” says Kumar.
He points out that these agents differ from typical chatbots in that they have lasting memory and specialised business skills. They are ready to tackle high-pressure tasks, such as:
- Loan & Credit Decisioning: Automating data entry and risk assessment.
- Treasury Management: Drastically reducing operational overhead through specialised assistance.
- Compliance: Performing rapid regulatory impact analysis when rules change.
3. The tug-of-war over compliance and oversight
While there’s a lot of excitement surrounding autonomous workflows, issues of trust and safety still present challenges.
Daniel Kroytor, CEO of TailoredPay, provides an essential perspective, balancing the excitement of agentic AI with the realities of regulatory implications.
“I think the use of AI in the fintech industry is still fairly limited due to compliance issues,” Kroytor argues. “If you make mistakes handling customers’ personal information, the consequences can be devastating. I don’t think AI is yet at a point where it can be trusted to make any judgment independently without oversight.”
However, David King, Head of Sales at AI Voice Solutions, suggests that agentic AI is the solution to, rather than the cause of, compliance headaches. In response to concerns about oversight, King notes that agents ensure “every action, decision, and handoff is logged and auditable by default.”
“Agentic AI is already showing real value… pushing approvals forward without relying on humans to chase documents,” King states. “The real shift here is that the agent isn’t just assisting the process. It owns the outcome – moving an application to an approved or declined state as efficiently as possible.”
4. Redefining liability in machine commerce
As agents start to “own the outcome,” as King proposes, the fundamental concept of financial liability needs a fresh look. Qi Cao, CEO of Chargeblast, cautions that the industry is nearing a critical juncture in how transactions are initiated.
“Until now, fintech systems have assumed that a human is always behind a transaction. That assumption is about to break,” says Cao.
He predicts that disputes will no longer stem solely from consumer confusion, but from “machine-driven behaviour that today’s rules weren’t designed to classify.” This necessitates a move toward preventative infrastructure rather than reactive dispute handling.
Kumar supports this view of proactive intervention, noting that agents will soon act as “banking fraud assistants,” capable of “identifying unusual patterns in real-time and taking immediate steps to block or flag transactions based on pre-set guardrails.”
5. From silos to multi-agent collaboration
By 2030, the fintech sector is expected to have matured into multi-agent systems. Kumar envisions a “Connected Intelligence” where specialised agents collaborate across silos.
“A sales agent in a CRM might communicate directly with a risk assessment agent in a separate system to pre-approve a client offer without human mediation,” Kumar explains.
The democratisation of data empowers business users to interact with legacy systems using natural language, effectively closing the gap between technical intricacies and strategic decision-making.
6. Modernising the legacy stack
Finally, for traditional banking institutions, agentic AI acts as a bridge to the cloud. The burden of legacy code – a long-standing hurdle for established banks – may finally be lifted by autonomous workers.
Kumar highlights that agents will “automate the conversion of legacy code… significantly speeding up modernisation efforts,” and even generate “privacy-compliant synthetic data” to test new products without exposing sensitive information.
The bottom line
Whether it’s Fozzati’s “dynamic insurance coverage,” Kumar’s “predictive models to identify attrition,” or Cao’s advocacy for “preventative infrastructure,” the future of fintech is leaning towards agents that go beyond mere conversation to include transacting, decision-making, and enforcement.
Experts agree on one thing: the financial system is shifting from reactive measures to a focus on enforcement and prevention through agentic AI.