Introduction
Artificial intelligence is increasingly moving beyond the role of a chatbot in business banking. Instead of simply answering questions about financial information, newer systems are being designed to interpret account data and eventually perform actions on behalf of business customers.
European financial management platform Finom is pursuing that model through Finom AI, an assistant embedded directly within its business account. According to Finom head of product Alex Gromadzki, the system can work with information already held within a customer’s account, including balances, transactions, invoices, cards and accounting data.
The company initially launched Finom AI in Germany, France, Italy and the Netherlands. Its development highlights a broader challenge for financial technology companies: moving AI from low-risk information retrieval toward actions involving payments, cards and other sensitive financial operations while maintaining customer control.
From Chatbot to Embedded Banking Assistant
Traditional AI assistants in financial services can operate largely as an additional interface layered over existing systems. A customer asks a question and the AI retrieves or explains information made available to it.
Finom’s approach is different in that the assistant is integrated into the business account itself. According to the company, this allows Finom AI to use a customer’s existing financial context when responding to requests.
For an SME owner, that can reduce the need to navigate multiple dashboards or manually provide background information before asking a financial question.
The company says users can ask questions about their actual business activity, including outstanding invoices, card spending, incoming and outgoing funds, active cards, accounting records and available card benefits.
That positions AI less as a generic financial chatbot and more as an interface for accessing information already contained within the business account.
What Finom AI Can Do Today
Finom says its current AI capabilities are focused primarily on helping entrepreneurs locate and interpret financial information.
Examples include asking:
- Which invoices remain unpaid?
- How much was spent on a particular card?
- What was a specific transaction for?
- How much money entered or left the account during a particular period?
- Which cards are currently active?
- Which bookkeeping records or reports are available?
- What cashback or card benefits are available?
These functions address a common operational problem for small businesses: financial information may already exist within banking and accounting systems, but finding and interpreting it can consume time.
The potential efficiency gain comes from replacing multiple navigation steps with natural-language interaction.
The Shift From Answers to Actions
The more significant development is the move from retrieving information to performing financial actions.
According to Finom, some actions are already available, including freezing a card. Other capabilities are being tested or developed, including creating and sending invoices, sending invoice reminders, preparing payments, paying known contacts, creating wallets and ordering cards.
This transition introduces a substantially different risk profile.
An incorrect answer about a transaction may create confusion. An incorrectly executed payment can directly move money from a customer’s account.
Finom therefore says its system uses different levels of user control depending on the sensitivity of an action. Lower-risk functions can require straightforward confirmation, while higher-risk financial operations may require stronger authorisation, including two-factor authentication.
Why Financial AI Requires a Different Trust Model
Business banking has a relatively low tolerance for errors.
Customers may accept an imperfect response from a general-purpose chatbot, but financial software operates in a different environment. Incorrect payment instructions, inaccurate cash-flow information or inappropriate accounting classifications can have direct business consequences.
Finom’s stated approach is therefore based on gradually increasing AI autonomy.
The company describes principles including exposing uncertainty instead of guessing, showing users the information underlying an AI response and requiring confirmation before high-impact actions.
This creates a distinction between assistance and delegation.
An AI system answering, “Which invoices are overdue?” does not necessarily need authority to change anything. An AI system preparing or executing a payment requires a substantially stronger control framework.
The closer AI gets to moving money, the more important authorisation, transparency and reversibility become.
Localisation Is a Major Challenge Across Europe
Finom initially deployed its AI assistant in Germany, France, Italy and the Netherlands, markets where the company already operates.
However, financial AI cannot necessarily be deployed across European markets simply by translating the interface.
Business customers operate within different tax systems, accounting practices, compliance requirements and financial conventions.
According to Finom, localisation in Germany involves considerations such as DATEV, Elster, VAT and detailed bookkeeping categories. Italy presents a different environment, including a strong role for accountants and its own tax requirements. France and the Netherlands similarly require adaptation to local compliance, language and financial-product expectations.
This means that financial AI requires more than language localisation. Its underlying logic and data interpretation may also need to reflect national financial systems.
AI and the SME Administrative Burden
Small and medium-sized businesses frequently operate with limited administrative resources. Owners may be responsible for monitoring cash flow, following up on invoices, managing employee cards and coordinating accounting information alongside running the underlying business.
An AI assistant that can consolidate these tasks could therefore have value beyond conversational convenience.
The potential benefit lies in reducing the number of separate processes required to obtain information or initiate routine financial tasks.
For example, an SME owner could potentially identify an overdue invoice and initiate a reminder without separately searching through an invoice-management interface. Similarly, payment preparation could eventually become part of the same conversational workflow used to identify an upcoming financial obligation.
The efficiency case depends on the system maintaining sufficient accuracy and appropriate controls.
From Reactive Assistant to Proactive Financial Tool
Finom’s longer-term roadmap also points toward a more proactive form of AI.
Rather than waiting for a customer to ask a question, the company says Finom AI is intended to monitor account activity and surface potentially relevant issues.
Examples cited by Finom include identifying an overdue invoice, highlighting a potential cash shortfall before a scheduled payment, identifying idle funds and helping customers plan for VAT or tax reserves.
This changes the interaction model again.
A conventional banking interface waits for the customer to check balances, invoices or upcoming payments. A proactive AI system could instead identify information it considers relevant and bring it to the customer’s attention.
The challenge is ensuring that proactive recommendations remain useful without becoming intrusive, misleading or overly confident.
Security and Authorisation Become More Important as AI Gains Control
Moving AI into financial workflows increases the importance of access controls and transaction authorisation.
The risk is not limited to whether an AI model produces an incorrect response. A system capable of taking action must also ensure that the correct customer is authorising the correct transaction under the appropriate circumstances.
Finom’s use of confirmation mechanisms and stronger authorisation for higher-risk actions reflects this distinction.
A staged approach can allow financial institutions and fintech companies to establish trust through relatively low-risk functions before expanding the range of activities an AI system can perform.
That principle is particularly important for irreversible transactions, where correcting an AI error after money has moved may be more difficult than preventing the transaction in the first place.
The Broader Implications for Digital Banking
Finom’s strategy illustrates a wider shift taking place across digital financial services.
The first generation of financial AI largely focused on customer support and information retrieval. The next generation is increasingly being designed around contextual understanding, workflow automation and financial actions.
For business banking, this could eventually make the account itself an operating environment where customers can query financial data, manage administrative processes and initiate transactions through a single interface.
But greater automation also increases the importance of governance. Financial institutions need to determine which activities can be automated, which require customer approval and which should remain subject to additional controls.
The competitive advantage may therefore come not simply from building a more capable AI model, but from integrating AI with reliable financial data and a carefully designed authorisation framework.
Outlook: From AI That Answers to AI That Acts
Finom’s roadmap suggests that business banking AI is moving toward a model in which the assistant does more than explain what is happening inside an account.
The progression is relatively clear: first understand the customer’s financial information, then provide relevant answers, next recommend actions and eventually execute approved tasks.
For SMEs, that could reduce the administrative burden associated with invoices, cards, payments and financial monitoring.
For fintech providers, however, the transition also raises the standard for reliability and control. The closer an AI system gets to moving money or making consequential financial decisions, the less acceptable unexplained errors become.
The emerging model is therefore not simply AI that pays, but AI that can act within clearly defined boundaries, with appropriate customer authorisation and visibility into what it is doing.
Conclusion
Finom’s integration of AI into its business banking platform reflects a broader evolution in financial technology, from conversational assistance toward workflow automation and approved financial actions.
The potential benefits for SMEs include faster access to financial information, less administrative work and greater integration between banking and accounting processes. But expanding AI’s role also increases the importance of transparency, authentication, customer approval and risk controls.
The next stage of business banking AI will likely be determined as much by how safely systems can act as by how intelligently they can answer.
