AI Agents in Banking and Fintech: Definition, Applications, and Perspectives

Artificial intelligence is enabling several experimental pilots in the financial sector to become an operational engine. In this transition, a concept emerges that is beginning to redefine how we understand automation: AI agents.

These are not simple chatbots or scripts with fixed rules. An AI agent is a system capable of interpreting an objective, deciding how to act, and executing concrete actions in complex environments such as payments, treasury, or fraud prevention. Its promise is not just efficiency: it’s strategic autonomy for financial processes that once required constant human intervention.

Today, most digital banking operations function under structured flows. That is, processes that are executed in a fixed sequence: each step depends on the previous one, and you can’t advance until it’s completed.

A clear example is a money transfer. Although for the user it’s enough to enter the destination account and amount, several mandatory stages happen behind the scenes:

1. The system receives the instruction and classifies the type of operation.
2. It validates that the account has sufficient funds and that there are no blocks or exceeded limits.
3. The payment engine selects the corresponding rail: ACH for interbank transfers, the internal network if it’s a transfer between accounts of the same bank, or in some cases, card networks when the destination applies.
4. The instruction is sent to the central clearing system.
5. Once processed, the movement is settled and recorded both at the issuing and receiving bank.

Each phase is predefined, like a closed chain where every step depends on the previous one. This guarantees control and consistency, but it also means that if an error appears —insufficient funds, a downed rail, or a detected risk— the flow doesn’t know how to adapt: it simply stops or rejects the operation.

On the other hand, a financial agent operates differently:

It receives the instruction (“transfer funds to the supplier’s account in Mexico”) and has the ability to:

1. Analyze the context: available balance, payment due date verification, fraud risk.
2. Choose the optimal route: determine whether to use account validation, select the payment method and banking rails according to cost, speed, and availability.
3. Execute and verify: complete the operation and adjust if it encounters blocks or rejections.

In other words, an agent doesn’t operate like a queue of predefined steps. It doesn’t need A to happen before moving to B and then C. It can evaluate the full picture and choose the best route: skip unnecessary steps, switch rails in real time, or even stop the operation if it detects a risk. This turns a rigid sequence into a flexible decision map, where the key is not following a script, but fulfilling the objective in the most efficient and secure way possible.

In banking and fintech, AI agents are starting to consolidate in three key areas:

1. Treasury optimization and multi-rail payments
   
   Companies with regional operations need to move liquidity between countries and accounts in real time.
   
   An agent can decide when, through which rail, and in what order to execute movements—prioritizing urgent payments and minimizing transaction costs.
   
   In architectures such as Prometeo’s Agentic Banking Infrastructure, this logic allows a single agent to manage the entire orchestration of A2A payments, with or without constant human intervention.
   
2. Fraud prevention and account validation
   
   Agents can combine multiple layers: query KYC/AML modules, validate bank accounts in real time (VoP/CoP), and automatically block suspicious operations until additional verification confirms the destination of the funds.
   
   In practice, this reduces fraud without slowing down the end-user experience.
   
3. Intelligent transactional support
   
   Beyond the customer support front, agents can resolve claims, issue receipts, retry payments, and update internal records without human intervention.
   
   Each interaction resolved autonomously represents less friction and more scalability for the bank or fintech.

The enthusiasm around AI agents has raised expectations, but not everything imagined today is feasible in production.

Overrated: consumer-oriented agents, such as those that promise to plan entire vacations, manage personal subscriptions, or make financial decisions without supervision.

Although they sound appealing, these tasks require defining every preference in detail and then manually reviewing that everything was executed correctly—which ends up being as complex as doing it yourself.

Underrated: internal and repeatable automations, those that save seconds or minutes but, when multiplied hundreds of times, transform operations.

Tasks such as reconciliations, liquidity monitoring, regulatory reporting, or iterative searches can be executed autonomously, reliably, and measurably—achieving real and scalable impact in banks and fintechs.

In practice today, the tangible value of the agentic wave lies in verifiable internal processes, not in spectacular promises for the end consumer.

The true takeoff of AI agents in banking and fintech will occur in two stages.

First, we’ll see agents specialized in repetitive but critical tasks: reconciliations, balance and liquidity monitoring, report generation, and even the execution of payments under multiple fixed rules. Processes that seem routine today but, once automated reliably, free up hours of human work and reduce errors.

Then, multi-agent financial ecosystems will emerge: one will execute payments, another will validate bank accounts and identity, another will assess risk or update documentation. These agents will collaborate to reason about operations in real time, not just execute rules. It will be the first step toward intelligent banking infrastructures capable of operating with distributed autonomy.

It’s expected that institutions beginning to experiment today will be able to scale their operations 10X or 100X without increasing costs or staff at the same rate.

The agentic wave doesn’t replace human oversight, but it turns financial operations into a living, adaptable system ready for the new digital era.

For banks and fintechs, the opportunity that AI agents open is enormous—but jumping into the hype without a method often ends in frustration. The experience of those already developing these solutions shows a viable pattern: success comes when operational muscle is built step by step.

The first step is to start simple. Initial projects should focus on processes that are easy to verify and don’t compromise core operations: internal reconciliations, reports, or activity monitoring. In early-stage companies, the cost of error is lower and results are more visible—this is when it’s best to experiment aggressively. Each automation tested at this stage not only saves time today but also prevents the bottlenecks that, at scale, can slow down entire operations.

The second is to measure everything from the start. It’s not enough for the agent to “work”; it must be proven whether it truly reduces time, avoids errors, and generates operational savings. Without a clear feedback loop, it’s easy to spend months developing something a simpler solution could have solved.

The third is to give agents the context they need to be effective. In practice, this means well-documented APIs, clean data, and clear descriptions of each available tool. A language model can be sophisticated, but if it doesn’t understand how to interact with its environment, its autonomy becomes limited.

Lastly, there’s product vision.

The most valuable agents aren’t those that rely on fragile tricks or unrepeatable configurations, but those that naturally improve as models evolve. If the next generation of AI makes your solution obsolete, you built in the wrong direction. On the other hand, if every leap in capability makes your agent more precise and faster, you’re creating an advantage that scales over time.

Integrating agents in banking and fintech isn’t about replacing human teams or dazzling with spectacular demos. It’s about building a reliable, measurable, and scalable foundation that allows financial infrastructure to operate more autonomously—without losing traceability or control.

And there’s no need to rush. 2025 will mainly be a year of trial and error: experimenting, measuring, iterating. It will take time to build trust in both technology and processes. But precisely for that reason, the current moment is strategic: each well-designed pilot accumulates learnings that later scale. Thinking of an agent as a mid-term project that doesn’t distract from immediate goals but gradually frees capacity and reduces friction is the most realistic way to capture its value.

AI agents mark the beginning of a new era in financial services: infrastructures that think, decide, and act—and whose impact isn’t measured only in efficiency, but in the ability for banks and fintechs to evolve from reactive operations to intelligent ecosystems capable of adapting to the market in real time.

In this future, the advantage won’t belong to those who automate faster, but to those who design their architecture to learn and decide with purpose. Human oversight will remain important, but true transformation will come when financial infrastructure stops waiting for instructions and begins to anticipate them.

In the new era of banking, the question won’t be whether agents will be used, but how ready each institution will be to delegate real decisions to these systems—safely, intelligently, and with purpose.