AI cash flow forecasting built from your bank feeds, open receivables, payables and ERP history. The model learns how your customers really pay and when costs really land, then explains every week's variance so treasury stops rebuilding spreadsheets.
Sample data. Forecast band narrows as the model learns customer payment timing.
AI cash flow forecasting is the use of machine learning to predict future cash receipts and payments from bank transactions, open AR and AP, payroll, tax and ERP history. Instead of applying fixed payment terms, it learns actual customer payment timing and recurring spend patterns. It outputs a rolling 13-week direct cash forecast by entity and currency, with confidence ranges and a weekly explanation of forecast versus actual.
Someone downloads bank balances, exports ageing reports, emails business units for their numbers and pastes it all together. By the time the forecast is reviewed, it is already out of date.
of $1B–$10B companies collect forecast data manually and consolidate it offline, per PwC's 2025 Global Treasury Survey of 350 treasurers.[1]
of treasurers cite poor data quality as a barrier to effective forecasting (PwC, 2025).[1]
Average satisfaction with manual forecasting versus 3.3 for system-based forecasting in the same PwC survey.[1]
Terms are not behaviour so a forecast that assumes customers pay on 30 days misses when they actually pay.
Pulls balances and transactions from your banks and open items from the ERP, then classifies every line into forecast categories.
Separate models for receipts, supplier payments and recurring flows, combined into a direct-method forecast with ranges.
A weekly pack showing forecast versus actual by driver, with scenarios for the decisions you are weighing.
We connect bank exports and ERP read access for one or two entities, and agree the metric, usually week-1 to week-4 forecast error.
The model forecasts past weeks it has not seen. We compare its error with your existing spreadsheet forecast for the same weeks.
The forecast refreshes each morning. Treasury adds known one-offs and reviews the first weekly variance pack.
We report live and back-tested accuracy against baseline by horizon, and agree whether to move to Run and add entities.
| Criterion | Spreadsheet | Off-the-shelf tool | Stratgik build + run |
|---|---|---|---|
| Data collection | Manual exports and emails | Automated for supported banks and ERPs | Automated, including local and regional banks |
| Receipts logic | Payment terms | Rules or vendor models | Customer-level ML trained on your history |
| Variance explanation | Analyst commentary | Category-level | Driver-level, customer and supplier |
| Proof of accuracy | Rarely measured | Vendor case studies | Back-test versus your current forecast |
| Best fit | One entity, stable cash | Standard banking and ERP stack | Multi-entity, multi-currency, lumpy receipts |
Interest in machine learning for forecasting is high, but few teams rate their capability as mature. Meanwhile, cash tied up in working capital keeps the stakes high.
A better forecast lets you hold a smaller safety buffer and stop rebuilding spreadsheets. The buffer reduction is an assumption until the pilot measures your forecast error.
Illustrative estimate using your inputs and stated assumptions, not a quote or guarantee. The pilot measures the real figure against your baseline.
21-day pilot, up to 2 entities and 5 bank accounts
per month, up to 3 entities
per month, group-wide multi-currency forecasting
Usage (model compute, bank connectivity fees, cloud hosting) billed at cost; deployed in your cloud account where required; taxes excluded. GBP and AED prices are indicative conversions from USD.
AI cash flow forecasting accuracy depends on your data history and how lumpy your cash flows are, so we do not quote a generic number. In the pilot we back-test the model on past weeks it never saw and compare its error with your current spreadsheet forecast for the same weeks, by horizon. You see the result before committing to anything further.
A 13-week cash flow forecast is a rolling, week-by-week projection of cash receipts, payments and closing balances for the next quarter, built using the direct method from actual transactions. Treasurers, lenders and restructuring advisers use it to manage liquidity, covenant headroom and payment timing. Each week, the oldest week drops off, a new one is added, and variances are reviewed.
It needs 12 to 24 months of bank transactions, open and historical AR and AP from your ERP, payroll dates and totals, and known schedules such as tax, rent, debt service and capex. More history helps the model learn seasonality and customer payment behaviour. Missing or messy data is common; the pilot shows which gaps matter and which do not.
No. Machine learning takes over the data gathering and the routine prediction of recurring flows, which is where most forecasting hours go. Your analyst still owns one-off items, business context, scenarios and the final forecast, and spends more time explaining variances and advising on decisions. The system never moves money or executes payments.
We read open AR, AP and historical transactions from NetSuite, SAP, Dynamics 365 or Sage, and bank data from statement files, host-to-host connections or aggregators. If you already use a treasury system such as Kyriba, we can feed our forecast into it or take its bank data, rather than replacing it. Outputs are also available in Excel and Power BI.
A first production forecast runs within 21 days for one or two entities: bank and ERP connections in week one, back-testing in week two, and live daily forecasts in week three. Adding more entities, currencies and banks usually takes a further four to eight weeks, mostly driven by how quickly bank data access is arranged.
A 30-minute call to choose entities and a metric, then a fixed-price 21-day pilot scored against your current spreadsheet.