Knowing Where Your Cash Will Be, Not Just Where It Is

Treasury lives on a question that's harder than it looks: how much cash will we have, and when? Get it wrong on the low side and you're borrowing expensively or scrambling. Get it wrong on the high side and you're leaving cash idle that could've been invested or paying down debt. The spreadsheet forecast most teams rely on is usually stale before it's finished.

Part of the problem is that cash flow is genuinely multi-source. It depends on when customers actually pay versus when they're due, on payables timing, on payroll cycles, on FX moves, on intercompany funding. Stitching that together by hand across entities and currencies is slow, and by the time you've done it the picture's moved.

What ML changes here

A machine-learning forecast ingests the feeds directly — ERP receivables and payables, bank transaction history, market data for FX and rates — and learns the timing patterns. It knows this customer segment pays about eight days late on average, that this month carries a payroll and a tax payment, that this receivable will likely land next week even though it's technically overdue. The output is a rolling forecast that updates as new data arrives rather than one you rebuild every Friday.

The real prize is visibility. When treasury can see the cash position weeks out with reasonable confidence, everything downstream sharpens — you borrow less on the revolver, you invest surplus instead of letting it sit, you fund entities more precisely instead of over-buffering every account "just in case." That over-buffering is trapped cash, and freeing it is money the CFO notices.

What to keep honest

Forecast accuracy varies by horizon. Next week is quite predictable; next quarter, less so — and the model should give you a range, not a false-precision single number. Treasury decisions are better made against "we expect between X and Y with this confidence" than against a point estimate that pretends to certainty it doesn't have.

Data connectivity is the unglamorous foundation. If your bank feeds are delayed or your ERP data on payment terms is dirty, the model inherits every gap. Multi-bank, multi-entity environments especially need the plumbing sorted first, and that's often the bigger part of the project than the model itself.

Large, lumpy, one-off flows are the model's blind spot — a major acquisition payment, a big tax settlement, a debt maturity. Those are known to treasury but invisible to a pattern-learner, so the human overlays them. The forecast should combine the model's read on the recurring flows with the treasurer's knowledge of the big discrete events.

And stress-test it. A cash forecast that only works in calm conditions isn't much use, because the moment you most need it is when conditions aren't calm. Run the scenarios — a slow-paying quarter, an FX shock — and see whether the forecast still guides good decisions. That's when forward cash visibility stops being a nice dashboard and starts earning its keep.