Artificial intelligence has already started changing the way companies manage their finances. But in treasury, more than a futuristic promise, the value lies in something much more concrete: saving time, reducing manual errors, and improving visibility into cash flow.

In many SMEs and mid-sized companies, treasury still operates with a mix of banks, ERP systems, Excel spreadsheets, WhatsApp, and manual follow-up. The problem is not just operational. When information is fragmented, the finance team loses speed, loses judgment, and, many times, also loses money.

The good news is that there are already real ways to apply AI in treasury without having to change the entire operation overnight. You can start with specific processes, create quick impact, and then scale up.

Why treasury is still so manual

Most companies do not have a data shortage problem. They have a problem of fragmentation and execution.

The transactions are in the banks. The invoices are in the ERP. The forecasts are in Excel. The urgent payments are in WhatsApp. Collections, many times, depend on human follow-up. And when it is time to understand consolidated cash or project liquidity for the next few weeks, the team has to assemble the puzzle by hand.

That is where one of the biggest bottlenecks in financial management appears: many hours dedicated to organizing information before being able to decide.

That creates several problems at once:

• slow or incomplete reconciliations

• little traceability over collections and payments

• outdated cash flow projections

• excessive dependence on key people

• operational errors due to manual entry

• low ability to anticipate cash shortfalls

AI does not magically solve a poorly designed process. But it can take on a huge part of the repetitive work that currently consumes the team’s time.

What AI can solve in treasury today

When people talk about AI applied to finance, they often fall into overly broad messages. In treasury, it is better to get concrete.

Today, AI can provide real value in five very specific areas:

1. Faster bank reconciliation

It can help link bank transactions with invoices, receipts, collections, or disbursements even when the reference does not match perfectly. It compares amount, date, text, and context to suggest reconciliations automatically.

2. Transaction classification

There are expenses and transactions that do not have a clear associated invoice or come poorly described from the bank. AI can identify patterns and classify items such as taxes, bank fees, payroll, loans, internal transfers, or supplier payments.

3. Cash flow projection

It is not only useful for looking at the past. It can also improve the quality of projections by detecting repetitive behaviors, typical collection dates, seasonality, and deviations from the expected plan.

4. Early alerts

When a company manages multiple accounts, currencies, or entities, the problem is often not a lack of information, but that no one sees it in time. AI can detect inconsistencies, significant deviations, or liquidity risks before they blow up.

5. Operational prioritization

Not everything has the same urgency. AI can help decide which collections to follow up on first, which payments have the greatest cash impact, or which tasks require immediate human review.

5 concrete use cases for AI in treasury

Let’s go a little deeper. These are some of the most concrete and valuable uses that can already be implemented.

Automatic reconciliation of collections and payments

One of the heaviest processes for any administration and finance team is reconciling bank statements against what appears in the management system.

The problem arises when reconciliation is not perfect: partial payments, approximate amounts, incomplete references, multiple invoices settled with a single transfer, or bank transactions that are not clearly described.

That is where AI can make a huge difference. Instead of forcing the user to review each transaction manually, it can suggest likely matches based on multiple variables.

That speeds up the process and significantly reduces operational friction.

Intelligent classification of bank transactions

There are transactions that are especially annoying to identify: automatically debited taxes, bank fees, grouped deposits, payroll, social security contributions, interest, card debits, or transfers between own accounts.

In many cases, the bank does not describe them well. Or it describes them with a logic that changes depending on the institution, the country, or the channel.

AI can learn these patterns and automatically tag transactions so they later flow better into reports, cash flow, or reconciliations.

This is key because bad classification does not just affect operational accounting. It also ruins the financial reading of the business.

More dynamic predictive cash flow

Many companies still project their cash in a static way: a spreadsheet, a weekly meeting, and manual adjustments based on what someone remembers or can review.

The problem is that reality changes all the time. A collection gets delayed. An unexpected expense comes in. A large payment is postponed. A key date changes. And the projection stops being useful.

AI can improve that in two ways. First, by updating projections with fresher operational information. Second, by detecting patterns that the human eye does not always catch quickly.

It is not about predicting the future. It is about projecting better, with less friction and more context.

Alerts about deviations and liquidity gaps

Many times the problem is not not having cash. It is finding out too late that there is a problem.

If an important collection did not come in, if an account became too exposed, if a business unit is draining more cash than expected, or if concentrated payments in one week create tension, the ability to respond depends on detecting it in time.

AI can help by generating smart alerts, not only based on balance, but also on expected versus actual behavior.

That point is critical: good treasury does not just record. It also anticipates.

Prioritization of collections and payments

In an operation with dozens or hundreds of transactions, not everything carries the same weight.

Some accounts receivable have larger amounts, greater age, or a higher likelihood of delay. Some payments can be moved and others cannot. Some cash decisions should be made before others.

AI can help organize that work and give the team a kind of priority map. Not to replace human judgment, but to focus it where it truly adds value.

What AI does not do on its own in finance

Here it is worth being serious. Because selling AI hype today is incredibly easy.

AI does not, by itself, replace a bad financial operation. It does not fix broken data by magic. It does not make up for disorganized processes. And it does not replace the judgment of the finance or treasury team.

If a company does not have even a minimal information structure, no integration between sources, and no basic defined processes, AI will not order the chaos on its own.

What it does very well is accelerate, suggest, detect, and automate on top of a reasonable operational foundation.

That is why the best approach is not to think, “AI will solve everything for us,” but to ask:

Which part of the manual treasury work is slowing us down the most today?

That is usually the best entry point.

How to start applying AI in treasury without breaking the whole operation

One of the most common mistakes is thinking that modernizing treasury requires changing the entire financial stack.

No. In fact, the best path is usually much simpler.

Start with a real pain point

Not with fashion. Not with marketing. Not with internal pressure. Start where the most time is lost or the most errors appear: reconciliation, classification, cash flow, accounts receivable, or payments.

Connect the key sources

AI alone does not work miracles if it lacks context. To work well, it needs to connect to the sources where the information lives: banks, wallets, ERP, billing system, or structured spreadsheets.

Measure operational impact

It is not enough to say, “we are using AI.” You have to measure whether something actually improved: hours saved, number of automatic reconciliations, fewer manual errors, better visibility, less delay in closing cash, or updating projections.

Scale gradually

Once a use case works, only then is it worth expanding it to other processes. That logic is much healthier than trying to transform the entire financial operation in a single stage.

The real change: less operational burden, more decision-making capacity

The best way to understand AI in treasury is not as a replacement for the finance team. It is as a layer that removes repetitive work so it can spend more time thinking.

Thinking about liquidity.
Thinking about payment timing.
Thinking about risk.
Thinking about scenarios.
Thinking about how to use the business’s cash better.

That is the real change.

When the team stops spending hours reconciling, chasing data, or correcting manual errors, it can start operating with more judgment and less wear and tear.

And in contexts where cash matters every day, that is not an operational detail. It is a competitive advantage.

How Fonder applies AI to treasury

At Fonder, we believe AI in finance makes no sense if it does not address concrete operational problems.

That is why we work on real treasury use cases: transaction reconciliation, intelligent classification, consolidated cash visibility, and projected cash flow, integrating banks, wallets, and management systems in one place.

The opportunity is not to make financial work more complex. It is to make it clearer, faster, and more actionable.

Because at the end of the day, treasury does not need more noise. It needs better information and better decisions.