$50k every 20 days

Let me tell you something, the average cost of unreliable cash flow forecasts is about $465,000 every year.

And what is worse, most finance teams rely on unreliable forecasts, with cash deficits of over $50,000 every 20 days on average! (Source: Agicap Survey).

After leading finance at Thales (10k+ employees) for 7 years, the big problem was always the time it took to forecast. Plus, the fact that the forecast was already wrong by the time we needed to make decisions.

Juergen Lang, (an expert in my AI Finance Club) decided to fix this.

He built a Python-based forecasting pipeline using a SARIMA model and Monte Carlo simulations (machine learning algorithms I’ll explain later) that re-forecast in seconds.

Saving a few hours is big, but to do this in seconds? This is super impressive.

He didn’t hire a data scientist. He used:

1. AI to write the code
2. His own finance domain knowledge to guide the model.

Today I’m going to show you exactly how he did it, and how you can build the same thing in 5 steps.

The problem CFOs aren’t measuring

So, let me ask you this.

How often do you download from your accounting system, open Excel, map your accounts, filter, copy to actuals, find budgets, calculate variances, format, then present…

Only to then find that:

Your customer delays payment by 30 days.
Your credit facility terms get renegotiated.
Your early payment discounts change.

And you have to rebuild the whole thing.

I call this ‘decision lag’.

It’s the time between when reality changes and when your forecast catches up.

In a calm year, you can manage a few days of lag.

But in 2026, with everything that’s happening geopolitically plus rate uncertainty, decision lag is probably the biggest cash management problem that most CFOs aren’t even measuring.

Think about it like this. You have a team member who did great work on Monday, then went on holiday for the rest of the month. The analysis was solid when they handed it in. But nobody’s updating it as things change.

You wouldn’t accept that from a person. So why accept it from a process?

10,000 Scenarios

Juergen is not a data scientist. He didn’t take a machine learning course. He’s a finance guy who understood his numbers and figured out how to make AI do the heavy lifting.

His system is actually straightforward. Data goes in: bank balances, AR/AP, his assumptions.

Then the model does two things:

• SARIMA: Looks at your past cash data and spots the patterns that repeat, like how collections always reduce in August or increase before year-end. It learns your trends, then projects it forward.
• Monte Carlo: Takes that projection and asks “but what if things don’t go exactly to plan?” You choose how many times to run it – it could be 1,000 or 50,000, but 10,000 is the sweet spot.

Juergen runs 10,000 versions of the forecast, each time changing the assumptions slightly.

Line them all up from worst to best and you get a probability range which is displayed as P numbers.

• P10 — only 10% of scenarios came in worse than this. Your realistic downside.
• P50 — the middle. Your expected case.
• P90 — 90% of scenarios came in below this. Your realistic upside.

So instead of telling the board “we’ll have $2.1M in cash,” you say “somewhere between $1.6M and $2.5M, most likely around $2.1M.”

That’s a conversation with the board that is much easier to plan around.

Jeurgen’s forecast updates in seconds. He runs it regularly for clients. And when assumptions change? Instead of re-building it, he just re-runs it.

His first version was a flat line (it didn’t work).

But he stuck with it, iterated, told the model about seasonality, excluded anomalies like COVID, adjusted for client-specific patterns etc.

After a few rounds, the model had a much better understanding of his data (based on his finance domain expertise) and he was ready to forecast forward.

He built this going through our AI Finance Accelerator program. So if you want to know how that works, just DM me. Happy to walk you through it.

Here’s how to do it yourself…

5-Steps to Faster Forecasting

Step 1 – List your drivers

Write down the 5-8 variables that move your cash: collections DSO, payables timing, payroll cycles, debt service, seasonal patterns. Plain English.

This is finance work, not coding. You’re telling the model what matters.

Step 2 – Generate the base model

Open your preferred (secure) AI tool.

Prompt the AI:

It writes the code. You don’t need to understand every line, you need to understand what it’s doing and why.

Before proceeding to the next step, make sure to copy the code. You will need it.

Step 3 – Open Google Colab and Paste Code inside

Open Google Colab in a new tab or by clicking here. Then create “New Notebook” to get started (make sure you are signed in).

Paste the code inside the cell below:

By the way – if you want to learn more on how to use Google Colab, you can check out my newsletter on how to use Machine Learning: Machine learning in 6 steps: How to make better decisions with finance data your brain can’t see.

Step 4 – Iterate with your knowledge

See the results Google Colab produces.

The first output will probably be wrong. So, go back to your AI, and push back the same way you would with a junior: “You’re not capturing Q4.” “Exclude 2020.”

Each round takes 10-15 minutes. After 3-5 rounds, you’ll have something that’s a lot better.

Just keep improving the code and re-running in Google Colab – Super easy.

Step 5 – Add Monte Carlo

Once you’ve got a working base model, you can now ask your AI to add in Monte Carlo simulations with a prompt like this:

Now when you re-run the updated code in Google Colab, you have probabilistic forecasting!

Not “what we expect” but “what range should we plan for.”

This is the use case that makes your board trust the model.

Super important – start with one driver only.

Collections is usually the best starting point because it’s the easiest to validate.

Get that working, show the board, then expand.

The One Thing To Remember

When you focus on systems instead of spreadsheets. Cash flow stops being a 2 day project.

So when you’re asked:

“What happens to our cashflow if collections are reduced by 10%?”

You can answer, in 2 minutes, in the meeting, with scenarios.

Plus (and this is super important) Juergen didn’t become a Python developer.

He just decided to become the finance professional who builds intelligent systems instead of requesting another spreadsheet.

Because, a forecast that’s only accurate on the day you made it isn’t a forecast. It’s just a receipt.

But, now you can build one that updates itself ;)​