Introducing cash forecast 2.0

Most finance teams can report revenue with confidence and still not say, with any certainty, how much cash will land next week. Revenue is booked the moment an invoice goes out. Cash arrives whenever the customer decides to pay. The gap between those two moments is where forecasting lives, and for most teams it is still a spreadsheet built by hand, using flat collection-rate assumptions that have little to do with how any specific customer behaves.

Today we're shipping cash forecast 2.0. Monk now produces a live, risk-weighted forecast of expected collections, grounded in the real payment behavior on every open invoice rather than static net terms. It updates continuously as customers reply, promise to pay, dispute, or send money, so the number a finance team looks at on any given morning reflects the receivables book as it stands.

Why we built it

Customers first come to Monk to stop chasing invoices by hand and to bring their DSO down. Once collections are running on their own, the next question is always the same: how much cash is coming in, when will it arrive, and which invoices are going to slip.

One finance director told us she owes a weekly cash forecast and cannot produce it reliably, because the work is manual and the underlying data is bad. Another customer was already building collection curves in spreadsheets, comparing invoice dates against payment dates to guess at timing. Cash forecast 2.0 turns that manual effort into a forecast Monk maintains automatically, built on how each customer has paid.

That shift is the point. When receivables run on agentic AI, finance leaders spend less time chasing and reconciling and more time forecasting and planning. The role moves from the back office to the front office, from reporting what already happened to deciding what to do next. For the teams already running this way, cash flow has become the growth metric they manage against.

How it works

Cash forecast 2.0 builds bottom-up from each invoice's real state instead of top-down from a blended rate. A few things make it work:

• A calibrated accuracy predictor. Monk scores how likely each invoice is to be paid and when, using payment history, current engagement, and account context. A customer who consistently pays 20 days late is not forecast the same way as one who pays on time.
• Expected cash, a forecast range, and cash at risk. Rather than a single number that hides its own uncertainty, the forecast shows the amount you can count on, the range around it, and the cash tied to invoices that are disputed or have gone quiet.
• Top accounts to follow up. The forecast surfaces the specific accounts most likely to move the number, so collections effort goes to the cash most likely to slip.

A few things we were deliberate about:

• The forecast covers open AR, the invoices that already exist. It does not predict future billing.
• It works from the remaining balance on each invoice and accounts for every adjustment along the way: partial payments, credit memos, write-offs, and refunds.
• It reads from the same platform that runs collections, so the promise-to-pay Julia captures from an email reply and the dispute a customer just raised both feed the forecast automatically. The result is one calibrated score for your cash rather than signals scattered across tools.

What it changes for a finance team

For a finance leader, the forecast moves the job from reporting to deciding. You get a forward-looking view of expected collections, timing, and risk, and you can act on it before the quarter closes rather than explain the miss after.

For a small team without a dedicated analyst, it delivers that same foresight without hiring for it or rebuilding a spreadsheet every week. The forecast is there, current, every morning. And it changes where effort goes: instead of chasing the oldest or largest invoices out of habit, teams can concentrate on the cash the forecast flags as most likely to slip.

Monk customers already describe that shift. "The analytics and reporting have made life so much easier when it's time to report to leadership. What used to be a manual build is now a quick export," said Liam Clements, Director of Finance at Goodship.

The context

There's a wider backdrop here, and it points straight at forecasting. Boards have already told finance to adopt AI: in a 2025 survey of 200 private equity sponsors and 200 portfolio CFOs, 98% of sponsors said they had directed their CFOs to prioritize AI, and 99% agreed the best way in is discrete finance workstreams such as automated close, cash flow forecasting, and invoice-to-cash automation (Accordion, 2025). Expectations for the forecast keep rising too, with 68% of companies reporting higher expectations for cash forecasting and only 1% reporting lower (PwC 2025 Global Treasury Survey).

The harder part is turning that adoption into value. Across finance, 59% of functions now use AI, yet 91% report only low or moderate impact early on (Gartner, 2025), and broader research finds most organizations still haven't turned AI use into measurable results, with strategic clarity the thing that separates the teams that do (BCG, 2026). Cash forecasting is a clear case of value from day one. It automates work a team was doing by hand and, at the same time, gives them a sharper read on the business, which is what turns a finance admin into a finance strategist.

Availability

Cash forecast 2.0 is live on the Monk platform now. Monk customers see a 40% or greater average reduction in DSO, and one customer, Profound, grew cash on hand by 122% in its first month after going live. Go-live typically takes one to three days, and Monk takes no percentage of the revenue it helps collect, so the economics scale with your business.

"Your forecast should not take hours to prepare, and it should reflect what your customers are doing throughout the collections process, in real time," said Joe Zhou, Co-Founder and CTO of Monk. "That is what cash forecast 2.0 gives a finance team. It reads every signal moving between you and your customers and turns it into a number you can plan around, and into the specific actions worth taking today."

See it on your own receivables: book a demo.

Related reading: The real reason your AR forecast is always wrong and billed vs. collected revenue.