AI for finance is already part of the daily work of many finance departments, not just banks: three in four organisations are actively applying it in areas such as financial planning, internal audit or risk management (KPMG, AI in finance 2026). In an area where an error turns into money, the useful question is no longer whether to use it, but on which tasks and with what controls.
What is AI used for in finance?
AI in finance is used mostly to prepare and to check: reconciling transactions, matching invoices to orders, bringing the month-end close forward, writing recurring reports and explaining variances against budget. In all of these the AI prepares the work and flags what does not add up, and someone on the team validates the result and signs it.
The benefit most often felt is speed: 70% of Spanish finance leaders single out AI's impact on the speed of analysis and of getting results (KPMG, AI in finance 2026).
Accounting and financial planning: where AI saves time
A finance department's work falls into two blocks, and AI contributes something different to each.
Automation and accounting
AI for accounting fits repetitive tasks with clear rules and high volume. Its value is in checking more cases in less time and flagging the exceptions, not in deciding the final entry.
| Task | What the AI does | What the person does |
|---|---|---|
| Bank reconciliation | Matches transactions and lists the ones that do not reconcile | Reviews the open items |
| Invoices and orders | Cross-checks amounts and data and flags discrepancies | Decides how to resolve each discrepancy |
| Month-end close | Prepares the task list and the draft adjustments | Approves the adjustments and closes the period |
| Recurring reports | Writes the first draft with the period's figures | Checks the numbers and signs the report |
Financial planning and analysis (FP&A)
In planning, AI helps prepare scenarios, summarise the causes of a variance and write the management-report commentary from the data. The interpretation stays with the team: the AI can suggest why a margin is falling, but whoever knows the business decides whether the explanation is right and what to do about it.
Treat it as a very fast junior analyst: useful for the first draft, and always reviewed before it reaches the board.
What controls does AI need in a finance department?
AI in a finance department needs three controls: traceability of what each agent does, human review proportional to the task's risk, and a use policy with approved tools. Without them, an AI error can reach a report or a payment with nobody knowing where it came from.
The record is the key piece. Every agent should leave a trace of who triggered it, what data it used and what result it returned. That record is what makes an agent defensible in an audit, but it only works if someone reviews it regularly.
Finance · The record
Four facts, on every run.
Who triggered it
The person or process that set it running.
What data it used
The sources it read and the period they cover.
What result it gave
The exact output, not a summary of it.
Who reviewed it
The person who validated it, and when.
Source: Ironhack for Business.
To decide how much review each task needs, in our AI consulting we use a three-level scale: AI executes and a person reviews, AI accelerates and a person validates, or a person leads and AI assists. A routine reconciliation sits at level 1; a closing adjustment or a forecast for the board, at level 3.
The controls also depend on people. Among finance leaders' main concerns are data quality and the workforce's ability to evaluate, interpret and act on AI-generated results (KPMG, AI in finance 2026).
We explain how they are governed according to their level of autonomy in our guide to AI agents.
What is the best AI for finance?
The best AI for finance is the one that integrates with your ERP and your spreadsheets, the one you already pay for in licences, and the one your team knows how to use on its own processes. It can be Claude, Microsoft Copilot or ChatGPT: in finance, the difference is usually less about the model than about access to reliable data and about permissions.
Before investing in a new tool, check which licences you already pay for and whether one of them covers the need. And above all, ask yourself one question: do you need to buy it, or can you build it? We explain it in Claude for business.
We learned this first-hand at Ironhack: with Claude we built our own landing-page tool in a week. We used to pay a five-figure annual subscription; today we have our own solution at barely 10% of that.
And it is not only landing pages. The same build-versus-buy analysis applies to reporting tools, financial reconciliation or process automation. The point is not to add more software, but to solve the same problem more efficiently: with more control and less cost.
We compare the two most used assistants in Claude vs ChatGPT. The verification controls finance needs look a lot like those of a legal team: we explain them in AI for lawyers.
How do you train a finance team in AI?
A finance team learns AI by working with its own processes and data, and by learning to review what the AI produces, not just to ask for it. That ability to evaluate results matters most in finance, because an error that passes review ends up in the accounts.
Our AI School includes a Finance and Administration track at three levels: foundational (using AI well), intermediate (building with AI) and advanced (running it in production). The tracks are built from each company's diagnosis, with real use cases that have an owner, a validated prompt and a KPI.
We measure tasks, not attendance: the hours a process takes before the training against the hours it takes 60 to 90 days later, valued at cost per hour and net of the FUNDAE training credit. We set the method out in the guide on the ROI of training.
Where to start with AI for finance: 4 steps
- Pick a repetitive process. A reconciliation, an invoice match or a monthly report. Measure how many hours it takes today.
- Define the controls before automating. Decide who reviews, what gets recorded and which level of the scale it belongs to.
- Train the team that will use it. Our hands-on workshops on productivity can be run per department, on Claude, Copilot or ChatGPT depending on the tools your company already has.
- Measure before you scale. Compare the hours the process took before and 60 to 90 days later. If the result improves and the errors are under control, move to the next process.
When a procedure works, save it as a Claude skill so the whole team repeats it the same way.
Next step
If you want to know which process in your finance department is the best candidate to start with, we will come back with a starting point in under 1 working day.