The Foundation

Introduction

Every treasury team wants AI cash flow forecasting. Almost nobody is ready for it.

Imagine a mid-size manufacturer. Twelve subsidiaries across eight countries. Eight currencies. The CFO walks into the quarterly business review and asks: “Where will our cash be in thirteen weeks?”

What happens next is usually a spreadsheet nightmare. Treasury sends emails to every subsidiary requesting their forecast. Half respond on time. The data comes in different formats. Someone forgot to include a tax payment. Another subsidiary submitted numbers in the wrong currency. The team spends two weeks consolidating, cleaning, and reconciling — only to produce a forecast that is already outdated by the time it reaches the CFO’s desk.

This scenario plays out in thousands of companies every month. It has played out for decades. But something has changed. AI, and more specifically agentic AI, has matured to the point where it can genuinely transform how treasury teams forecast cash flows — as a practical tool that delivers measurable results.

At Automation Boutique, we have been building AI solutions for treasury and finance since the technology became mature enough to deliver real value. We have seen what works, what does not, and where most companies go wrong. The pattern is almost always the same: they jump straight to the AI part and skip everything that makes AI actually work.

AI cash flow forecasting is not magic. It is a system. A system that starts long before any model runs its first prediction. And if you get the foundation wrong, no amount of AI will save you.

This is the playbook. Step by step. From raw bank data to autonomous treasury operations. Whether you build it yourself, use spreadsheets, or use a platform like NineAnts, the principles are the same.

The business case: why cash flow forecasting matters

Cash flow forecasting is consistently ranked as the number one challenge in corporate treasury. Not payments. Not bank relationship management. Not FX hedging. Forecasting. Why? Because it is the foundation for almost every other treasury decision — and most companies are still doing it poorly.

Idle cash earns nothing. If you do not know that you will have €10M of surplus cash for the next sixty days, that money sits in a current account earning zero. At a conservative money market rate of 3%, that is €50,000 in missed yield in just sixty days. Multiplied across a year, the numbers become significant.

Emergency funding is expensive. When a cash shortfall catches you by surprise, you do not negotiate from strength. The spread between planned and emergency funding can easily be 200 basis points. On a €20M shortfall lasting sixty days, that is €65,000 in unnecessary interest — for a single event. With a reliable forecast, you can often smooth cash flows internally before going to the market at all: postponing non-critical payments, swapping liquidity across currencies, or using cash intercompany to cover short-term gaps.

Unhedged FX exposure hits the P&L. If your forecast does not accurately capture the timing and size of cross-currency cash flows, you cannot hedge effectively. A 5% adverse move on €10M of unhedged USD exposure is €500,000 directly off your bottom line.

And perhaps most damaging: poor forecasting means poor visibility into business health. Cash is the ultimate truth-teller. When customer receipts start declining or supplier payments start stretching, those are early signals. Without a reliable forecast, management discovers problems too late.

Companies with accurate, timely cash flow forecasts consistently achieve better investment yields, lower funding costs, tighter FX risk management, and earlier warning of business issues. Treasury stops being a reporting function and becomes a strategic partner. For most mid-to-large corporates, the annual value of improving forecast accuracy by even 20–30% runs into hundreds of thousands, sometimes millions of euros or dollars.

Look back before you go forward

Before you forecast a single dollar or euro, you need to understand your past. Historical transactions and bank account balances are the raw material of any cash flow forecast. Without them, you are guessing. With them, you are learning.

Aim for at least 24 months of historical transaction data. Why 24 months? Because seasonality — the recurring patterns that follow a calendar rhythm — only reveals itself when you have enough history to compare year over year. Tax payments hit in specific months. Customer receipts spike in Q4. Supplier payments cluster around quarter-ends. None of this is visible in three months of data. Thirty-six months is even better — more history makes it easier to distinguish real patterns from one-off anomalies.

For bank connectivity, the two main approaches are Host-to-Host (H2H) via SFTP (CAMT.053 or MT940 file drops onto a secure server, batch-based but reliable) and API connectivity (real-time or near-real-time, accelerated in Europe by PSD2 and Open Banking). For multinationals, SWIFT messaging adds another option for global banking partner networks. Most groups end up using a mix. Whatever the method, automate the pipeline and normalise the data into a single format. CSV uploads are a fallback, not a strategy.

Then categorise. Raw transactions are noise; categorised transactions are signal. Typical categories include Customer Receipts, Supplier Payments, Salaries, Taxes, Capex, Intercompany flows, Bank Fees, Interest, Rent, Utilities, Loan Drawdowns and Repayments, and Dividends. Make them specific to how your business actually operates — a SaaS company and a manufacturer have very different cash flow profiles.

Labelling every historical transaction is the hard part. Excel’s Power Query engine can handle rule-based categorisation for free; it works but requires a lot of rules and a lot of maintenance. The more scalable approach is hybrid: rule-based categorisation for obvious patterns, with AI classification picking up everything the rules miss. Watch out for intercompany transactions (a payment from your Dutch entity to your German entity can look like a regular supplier payment), refunds and reversals, and multi-currency transactions where the same supplier appears with different names depending on which bank is processing the payment.

Finally, choose your time buckets carefully. Daily liquidity needs a day-by-day view. Medium-term planning often works better weekly. Board reporting and long-range outlooks read better monthly or quarterly. A good forecasting system lets you switch between these views — store data at the most granular level (daily) and aggregate up. Once you flatten the data at source, you cannot go back.

Bring in what you already know

Your bank data tells you what happened. Your business systems tell you what is going to happen. Open invoices from your ERP, scheduled payroll runs, known tax obligations, lease payments, loan repayments — these are cash flows where the amount and timing are already largely determined.

But ERP data is not always as clean as it looks. Invoice due dates are not payment dates: your ERP says an invoice is due in 30 days but this customer historically pays in 45. A well-designed forecasting system learns this difference over time. Partial payments, credit notes, and disputes muddy the picture further. And different ERP systems (SAP, Oracle, Microsoft Dynamics) model payment terms differently, so consolidating across multiple ERPs requires normalisation.

Known cash flows form the baseline of your forecast. For the near term, they are usually quite accurate. But three months out, your known cash flows might cover only 30% of what will actually happen. That is where AI comes in.