Accounts receivable dashboard: 3 examples, the KPIs to include, and how to build one

An accounts receivable dashboard is a one-page view of what your customers owe you, how late it is, and how quickly it is turning into cash. A useful one shows six to eight tiles: total receivables, the share that is overdue, overdue balances by age, days sales outstanding (DSO), average days delinquent, collection effectiveness, your largest overdue customers, and open disputes. Who reads it decides what goes at the top. A collector needs today's calls, a finance manager needs the week's trend, and a CFO needs the monthly picture of cash tied up in unpaid invoices.

This guide shows three example dashboards built on the same sample data, the tiles to include and how to calculate each one, and how to build your own in Excel or Google Sheets. It also covers how to read the numbers and act on them.

What is an accounts receivable dashboard?

An accounts receivable (AR) dashboard turns your invoice data into a small set of numbers and charts you can read in a minute. It sits on top of two things you probably have already:

• Your aged receivables report, which lists every open invoice by how overdue it is. The accounts receivable aging report guide explains how to read one.
• Your AR metrics, such as DSO and the collection effectiveness index. The guide to 10 accounts receivable KPIs defines each one in full.

The report is a list and the KPIs are formulas. The dashboard is where you put the few that matter side by side, with a trend and a target, so you can see what changed and decide what to do next.

Dashboards are usually grouped by who uses them and how often. Databricks describes operational dashboards that track activity as it happens, tactical dashboards that track a department over weeks to a quarter, and strategic dashboards that give leaders a high-level view against long-term goals. An AR dashboard can be any of the three, and the examples below cover one of each.

What should an accounts receivable dashboard include?

Start with the eight tiles below. Most teams need all of them somewhere, but no single view needs every one. The formulas follow the Credit Research Foundation's performance measures, which are a common reference for credit and collections teams.

Useful extras, once the core is in place, are the percentage of receivables over 90 days past due, cash collected against your forecast, the accounts receivable turnover ratio, and bad debt written off. For how to improve each metric, see the guides to calculating DSO, average days delinquent, and the collection effectiveness index.

The sample data behind the examples

All three examples below use one sample business, so you can see how the same numbers look to different readers. It made $1,170,000 USD of credit sales over the last 90 days, which is $13,000 USD a day. Today it has $520,000 USD of open invoices: $338,000 USD not yet due and $182,000 USD overdue.

3 accounts receivable dashboard examples

Each example below is an illustration built on the sample data above, not a screenshot of any product. Use them as layouts to copy, then swap in your own numbers.

Example 1: the collector's daily dashboard

Illustrative example with sample data.

This is an operational dashboard. It answers one question each morning: who do I contact today, and about what? It doesn't need DSO or a six-month trend. It needs a short queue.

• Overdue invoices to chase today, with the total value, so the collector can start with the largest or oldest.
• Promises to pay due today, so a missed promise gets a same-day follow-up rather than a new reminder a week later.
• Open disputes, with the oldest one's age, because a disputed invoice won't pay until someone resolves the query.
• Invoices falling due this week, so a friendly reminder goes out before they become overdue.
• The top overdue customers, with the oldest invoice for each. In the sample, Customer D owes less than Customer A but is 95 days past due, so it goes to the top of the call list.

Refresh it every morning. If the data is a day old, the collector will chase invoices that were paid yesterday.

Example 2: the finance manager's weekly dashboard

Illustrative example with sample data.

This is a tactical dashboard. It tells the person running the team whether collection is on track this week and where to step in. Every tile compares this week with a forecast or with last week, because a number on its own tells you very little.

• Cash collected against forecast. $94,500 USD came in against a forecast of $101,000 USD, a variance of ($94,500 − $101,000) ÷ $101,000 = −6.4%.
• Total overdue and its movement. Overdue rose by $7,500 USD, from $174,500 USD last week to $182,000 USD.
• Average days delinquent, up from 13 to 14 days. Customers are paying slightly later.
• Overdue by age. $104,000 USD is 1–30 days past due, $44,000 USD is 31–60 days, $18,000 USD is 61–90 days, and $16,000 USD is over 90 days. The last two together are $34,000 USD, or 6.5% of total receivables.
• Concentration. The top five overdue customers owe $98,000 USD, which is 54% of the $182,000 USD overdue. Most of the week's risk sits with five accounts.

Read together, these say the shortfall isn't spread evenly. Receipts missed forecast, lateness ticked up, and five customers hold over half the overdue balance. The action is to review those five accounts with the team this week, rather than to send more reminders to everyone.

Example 3: the CFO's monthly dashboard

Illustrative example with sample data.

This is a strategic dashboard. A CFO wants to know how much cash is tied up in receivables, whether that is improving, and what it would be worth to improve it further. Fewer tiles, longer trends.

• DSO against target. DSO is 40 days against a target of 38.
• The cash value of a DSO day. At $13,000 USD of credit sales a day, each day of DSO holds about $13,000 USD of cash. Reaching the 38-day target would release about $26,000 USD.
• Best possible DSO beside actual DSO. Best possible DSO is 26 days, so 14 of the 40 days come from late payment. That is the part collections can change without touching payment terms.
• Collection effectiveness and debt over 90 days, at 68% and 3.1% of total receivables. The first shows how thoroughly the team collects. The second is an early warning for bad debt.
• A six-month trend. DSO fell from 44 to 40 days while average days delinquent fell from 18 to 14. Because best possible DSO held at 26, the improvement came from customers paying sooner, not from a change in sales or terms.

A monthly view like this belongs in the board pack. Keep the definitions fixed from month to month, or the trend line means nothing.

How to build an accounts receivable dashboard in Excel or Google Sheets

You can build every tile above with standard spreadsheet functions. The steps below work in both Excel and Google Sheets. Use two tabs: an Invoices tab with your data, and a Dashboard tab with the tiles and charts.

Step 1: export one row per invoice

Export open and recently paid invoices from your accounting system into the Invoices tab, one row per invoice, with headers in row 1. You need these columns:

• A: customer
• B: invoice number
• C: invoice date
• D: due date
• E: invoice amount
• F: amount paid
• G: status, where you mark "Disputed" if a customer has raised a query

Keep at least the last 90 days of invoices, paid or not, because DSO needs your credit sales for the period as well as what is still open.

Step 2: add three helper columns

Add these formulas in row 2 of the Invoices tab and copy them down.

Step 3: calculate the tiles with SUMIFS

On the Dashboard tab, SUMIFS adds up the balances that meet one or more conditions. It works the same way in Excel and Google Sheets. Put credit sales for the last 90 days in cell B1 first, because both DSO tiles divide by it.

Step 4: add charts and a top-customer list

Chart the age buckets as a bar chart, so the older buckets stand out. For the top overdue customers, build a pivot table from the Invoices tab with customer as rows and balance as values, filtered to invoices with days overdue above 0 and sorted largest first. Microsoft explains how to create a PivotTable in Excel, and Google explains how to create and use pivot tables in Sheets.

In Google Sheets, the QUERY function does the same job in one formula:

=QUERY(Invoices!A:I,"select A, sum(H) where I > 0 group by A order by sum(H) desc limit 5 label sum(H) 'Overdue'",1)

Step 5: keep a monthly history tab

A spreadsheet only knows today's balances. To draw a DSO trend or calculate collection effectiveness, you need last month's numbers too. On the last working day of each month, copy total receivables, current receivables, credit sales for the month, DSO, and average days delinquent into a History tab as values, not formulas. Your trend charts read from that tab, and next month's beginning receivables come from it.

Step 6: add targets and thresholds

Give each headline tile a target and color it when it misses. A DSO of 40 days means little alone, but 40 against a target of 38 tells you where you stand. Use conditional formatting to turn a tile amber or red when it moves the wrong way, and keep the thresholds the same from month to month.

How to read an accounts receivable dashboard and act on it

A dashboard earns its place when it changes what your team does next. Look at tiles in pairs, because one number rarely tells you why it moved. The CRF notes that DSO's value lies in helping you tell whether a change in receivables comes from a change in sales or from something else, such as a change in selling terms. Best possible DSO and average days delinquent are what let you tell the two apart.

Late payment is the normal case, not the exception. In Chaser's 2026 accounts receivable report, 92% of businesses said their invoices are typically paid after the due date, and 17% said they wait more than 30 days. That is why average days delinquent and the age buckets deserve a place on every view, not just the collector's.

Common AR dashboard mistakes

• Too many tiles. If a tile doesn't lead to an action for the person reading it, move it to another view or drop it.
• No trend or target. A single month's DSO can't tell you whether things are getting better or worse.
• Stale data. A dashboard refreshed monthly can't run a daily chase queue.
• Mixed currencies. Convert every balance to one base currency before you total it, or the totals mean nothing.
• Averages that hide customers. A healthy DSO can sit on top of one large account that is 90 days late. Always pair averages with the top overdue customers.

When does a spreadsheet dashboard stop being enough?

A spreadsheet dashboard is a good way to decide which numbers matter. It becomes hard to keep up when:

• refreshing the export, the formulas, and the history tab takes hours every week
• the numbers are out of date by the time anyone reads them
• several people need the same view, and copies start to disagree
• you can see a problem on the dashboard but can't click through to the invoices and act on them

Chaser connects to your accounting system, so its reports update as invoices are raised and paid. You can follow DSO month by month, see how much is outstanding and overdue, review your largest debtors with the age of what they owe, and track chasing activity by email, SMS, auto-call, and letter against what has since been paid. The overview of Chaser's receivables insights shows what these reports cover.

Two features take the dashboard further. Receivables forecasting shows when cash is likely to arrive, using your live invoice data and each customer's payment history. AI debtor risk insights show which customers are most likely to pay late and who to follow up with first. Together they turn the dashboard from a record of what happened into a plan for what to do next, and free your team to spend its time on the conversations that bring cash in.

FAQs

KPI definitions follow the Credit Research Foundation's performance measures. Dashboard types follow Databricks. Spreadsheet functions checked against Microsoft Support and Google Docs Editors Help. Late payment figures from Chaser's 2026 accounts receivable report. The dashboard examples use sample data, and every figure was recalculated before publication. Last reviewed 25 September 2026.