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Coming soon: The cash flow forecast dashboard is not yet available in Daylit. This page describes the planned experience.
Why this exists: To predict actual incoming cash flow accurately, rather than relying on invoice due dates which customers frequently ignore. User Story: Maria (VP Finance) at Acme Corporation wants to see an AI-driven cash flow forecast for the next 13 weeks so she can make accurate payroll and purchasing decisions. Value: By factoring in historical payment behavior, the forecast provides a highly accurate picture of when cash will actually land in your bank account. This prevents cash crunches and allows for confident financial planning. Example: An invoice from Apex Solutions is due next week, but the AI knows Apex Solutions pays late when under liquidity pressure. Maria (VP Finance) sees the cash flow forecast shift expected revenue three weeks out — preventing her from overcommitting on next week’s payroll planning.

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Flowchart

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What the forecast shows

The cash flow forecast is built around a 13-week rolling window, starting from the next Monday after your request date. For each week in that window, the forecast displays three data series: Invoice inflows by due date (green). The total balance of open invoices whose due dates fall in that week. This represents the amount you are contractually owed during that period. Invoice inflows by predicted date (red). The total balance of open invoices that the AI predicts will actually be paid during that week, based on the invoice_pay_date_prediction insight for each invoice. Trailing 6-month average weekly inflow (black line). The historical average of weekly cash receipts over the past six months, calculated from your actual payment records. When the red (predicted) bars fall noticeably behind the green (due date) bars — or lag by one or more weeks — it indicates that a meaningful portion of your receivables are likely to be paid late relative to terms.

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How AI payment predictions work

Daylit trains payment date prediction models on your invoice and payment history. For each open invoice, the model generates a predicted_date — the date by which it expects the invoice to be paid. These predictions factor in each customer’s past payment behavior.
Prediction accuracy improves over time. In the early weeks after connecting your accounting integration, the model may rely on due dates as a fallback for customers with limited payment history. As payments are recorded, the AI refines its estimates.

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Interpreting the forecast chart

Your customers are predicted to pay later than their due dates. The gap between the green and red bars represents collections that will likely shift into later weeks.
Your customers are predicted to pay close to their due dates, which indicates good payment discipline across your portfolio.
Predicted inflows for those weeks are below your historical average. This may reflect seasonality or customers with poor payment history.

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Customer breakdown table

Below the chart, a table lists every customer with open invoices that fall within the 13-week window. The table is sorted by total predicted inflow, largest first, making it easy to identify which customers represent the biggest portion of your expected collections.