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The AR dashboard is your central view into the health of your receivables portfolio. As soon as you open it, you see your total outstanding balance, how your invoices are aging, which customers are flagged by AI signals, and what collection actions are queued — all without switching between screens or running a separate report.

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What the dashboard shows

The dashboard surfaces four categories of information at a glance: Total AR balance. The headline figure shows your total outstanding receivables as of the most recent data sync. Below it, your current A/R percentage tells you what share of that balance is still within payment terms — a quick indicator of overall portfolio health. Aging summary. A donut chart breaks your receivables into five buckets: Current, 1–30 days, 31–60 days, 61–90 days, and 91+ days past due. Each slice shows the dollar amount and percentage so you can see at a glance where overdue balances are concentrating. AI signals. Daylit’s AI continuously evaluates your customers and invoices for payment risk signals — things like late payment patterns, changes in payment behavior, or high concentration in a single account. High-severity and medium-severity signals are surfaced on the dashboard so you can act on the most pressing risks first. Portfolio health score. A composite health status (Healthy, Monitor, Concern, or Critical) is calculated from three factors: your current A/R percentage, your 91+ day exposure percentage, and your top customer concentration. Each factor has a defined threshold, and the status updates automatically as your data refreshes. The dashboard is organized into several areas, each accessible from the main navigation:
The AR Aging tab shows your full aging report with a summary view and a per-customer breakdown. You can drill into individual customers, view aging trends over time, and export the report as a PDF or CSV. See AR aging report for a full walkthrough.
The customers table lists every customer with an open balance, along with their total AR, aging breakdown, and any active AI signals. You can filter by risk level, sort by balance, and click into a customer profile for full details. See Managing customers and Signals and insights.
The invoices table shows all open invoices with their due dates, balances, and AI-predicted payment dates. You can filter by aging bucket or customer, and the predicted payment date column shows when the AI expects each invoice to be paid. See Managing invoices and Payment predictions.
The action center (also called the inbox) surfaces recommended collection actions — follow-up emails, escalations, or sequence enrollments — based on AI analysis of your open invoices and customer signals. See Action center for details.
The Analytics section includes a 13-week cash flow forecast that predicts when your open invoices are likely to be paid, broken down by week and by customer. See Cash flow forecast for a full walkthrough.

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How data stays current

Dashboard data is sourced directly from your connected accounting integration (QuickBooks, NetSuite, Xero, and others). Daylit syncs invoice, customer, and payment data from your accounting system on a regular schedule, and the dashboard header shows when data was last refreshed. You can also trigger a manual sync from the dashboard header if you need an immediate update.
If the dashboard shows a “stale” badge next to the report date, the most recent sync has not yet completed. The data shown reflects the last successful snapshot and remains accurate as of that date.
After each sync, Daylit recalculates aging snapshots, recomputes AI signals, and updates your cash flow forecast — so all sections of the dashboard reflect the same point-in-time data.

AR aging report

View overdue balances by time bucket, drill into per-customer aging, and export your aging report.

Cash flow forecast

See AI-predicted payment dates for open invoices across a 13-week horizon.

Signals and insights

Understand how Daylit surfaces payment risk signals across your customer portfolio.

Action center

Review and act on AI-recommended collection actions from a single queue.