How to Create an AR Aging Report in Looker with AI

An accounts receivable (AR) aging report is one of the most important financial health indicators for your business, yet building one can feel like a chore. You need to see who owes you money, how much, and for how long. This article will show you how to create an AR aging report in Looker, first by walking through the traditional manual process and then by exploring a much faster, AI-driven alternative.

Graphed

Still Building Reports Manually?

Watch how growth teams are getting answers in seconds — not days.

Watch the Demo

What is an AR Aging Report?

An accounts receivable aging report is a summary of all your unpaid customer invoices, organized into categories based on how long they've been outstanding. It's a fundamental tool for managing cash flow and assessing the financial health of your company.

The "aging" part refers to sorting these invoices into time-based buckets, typically:

• Current: Invoices that are not yet due.
• 1-30 Days Past Due: Invoices that are 1 to 30 days overdue.
• 31-60 Days Past Due: Invoices that are 31 to 60 days overdue.
• 61-90 Days Past Due: Invoices that are 61 to 90 days overdue.
• 91+ Days Past Due: Invoices that are more than 90 days overdue.

This report helps you instantly identify which customers are paying on time and which are becoming a credit risk. Consistently monitoring it allows you to be proactive with collections, forecast cash flow more accurately, and make smarter decisions about extending credit to customers.

How to Manually Create an AR Aging Report in Looker

Looker is a powerful BI tool, but unless the groundwork has been laid by a data team, you can't just build an AR aging report out of the box. The logic for the date buckets needs to be defined in Looker's modeling layer, LookML. For business users, this means you’re often dependent on a developer or analyst to get the initial report set up.

Here’s a breakdown of the typical process.

Graphed

Still Building Reports Manually?

Watch how growth teams are getting answers in seconds — not days.

Watch the Demo

Step 1: Set Up the Required Logic in LookML

Before you can build anything in Looker's user-friendly "Explore" interface, a developer needs to write some LookML code. This code defines the dimensions and measures available for analysis. For an AR report, you'll need access to customer names, invoice dates, due dates, and amounts from your underlying data source (like QuickBooks, Stripe, or your company’s database).

Calculating Days Past Due

First, your data team needs to create a dimension that calculates the number of days each invoice is past its due date. The code in the LookML view file would look something like this:

dimension: days_past_due {
  type: number
  sql: DATE_DIFF(${TABLE}.due_date, CURRENT_DATE(), DAY) ,,
  hidden: yes  # Usually hidden since it's just for calculation
}

Creating the Aging Buckets

With that calculation in place, the developer can now create the aging "buckets" using a CASE statement. This dimension categorizes each invoice based on its days_past_due value.

dimension: aging_bucket {
  type: string
  case: {
    when: {
      sql: ${status} = 'paid' ,,
      label: "Paid"
    }
    when: {
      sql: ${TABLE}.due_date >= CURRENT_DATE() ,,
      label: "Current"
    }
    when: {
      sql: ${days_past_due} <= 30 ,,
      label: "1-30 Days"
    }
    when: {
      sql: ${days_past_due} <= 60 ,,
      label: "31-60 Days"
    }
    when: {
      sql: ${days_past_due} <= 90 ,,
      label: "61-90 Days"
    }
    else: "91+ Days"
  }
}

Once this LookML code is saved and pushed to production, the Aging Bucket dimension becomes available for business users in the corresponding Looker Explore.

Graphed

Still Building Reports Manually?

Watch how growth teams are getting answers in seconds — not days.

Watch the Demo

Step 2: Build the Report in a Looker Explore

Now that the backend work is done, you can create the report from Looker's user interface.

1. Navigate to the Correct Explore: Open the Explore that contains your invoice and customer data.
2. Select Dimensions and Measures:
3. Filter for Unpaid Invoices: You only want to see outstanding invoices. Add a filter for Invoice Status and set it to exclude "Paid".
4. Run the Query: Click the "Run" button to generate the data table.

Step 3: Visualize the Data

A data table is useful, but a visualization can make the insights much clearer. A pivot table or stacked bar chart are great options for an AR aging report.

Option A: Use a Pivot Table

A pivot table gives you a classic accounting-style AR report.

• Make Customer Name the row dimension.
• Use the "Pivot" button on the Aging Bucket dimension to make its values the column headers.
• The values in the table will be the Total Invoice Amount for each customer within each aging bucket.

This gives you a crisp, spreadsheet-like view that clearly shows how much each customer owes in each time category.

Option B: Use a Stacked Bar Chart

A visual chart can help you quickly spot the biggest offenders.

• Choose "Bar Chart" from the Visualization tab.
• Set Customer Name on the Y-axis and Total Invoice Amount on the X-axis.
• Use the "Stacking" option and select "Stack" or "Stack - 100%".
• Pivot by the Aging Bucket dimension to get stacked segments representing each time bucket. Color-code them so that older debt (e.g., 91+ Days) stands out in a bright red.

Once you are happy with the report, you can save it as a Look or add it to a dashboard for recurring viewing.

The Challenges of the Manual Approach

While the final report in Looker is powerful, the process to get there highlights a few common frustrations for business teams:

• Dependence on Technical Teams: If you're a finance manager or business owner, you likely can't write LookML. To create the Aging Bucket dimension or even just tweak it (say, to add a "91-120 Day" bucket), you have to file a request with the data team and wait. This creates bottlenecks and slows down analysis.
• It's a Rigid System: What if you want to quickly see aging buckets based on the invoice date instead of the due date? That requires another LookML change. You can't just modify the logic on the fly. The structure is fixed until a developer updates it.
• Significant Learning Curve: Even without writing code, learning to navigate Looker's Explores, pivots, and visualization settings takes time. For teams already stretched thin, dedicating hours to learning another complex BI tool often isn't feasible.

A Simpler Way: Creating an AR Report with AI

Imagine if you could skip the LookML entirely. And the Explore interface. And the visualization settings menus. What if you could just connect your data and ask a question in plain English?

Modern AI-native tools shift the process from 'building' to 'asking.' Instead of manually clicking, selecting, and configuring, you can state what you need in a conversational prompt. For example:

“Create an AR aging report showing total unpaid invoice amount by customer. Bucket the days past due into 0-30, 31-60, 61-90, and 91+ days.”

An AI data analyst would instantly understand the request, perform the necessary calculations, and generate the corresponding visualization - whether a pivot table or a bar chart.

Graphed

Still Building Reports Manually?

Watch how growth teams are getting answers in seconds — not days.

Watch the Demo

Answering Follow-Up Questions Instantly

The real power of this approach shows itself in the follow-up. An AR aging report almost always leads to more questions. With a manual setup in Looker, each new question might require you to re-configure the report, apply new filters, or even go back to the data team.

With a conversational AI tool, you can simply keep asking:

• "Which 5 customers have the highest amount in the 91+ day bucket?"
• "Convert this into a stacked bar chart."
• "Now, filter this to only show customers based in California."
• "What's our total overdue receivable amount?"

This flow of conversation turns data analysis from a tedious task into an interactive discovery process. It removes technical barriers and empowers anyone on the team to get the financial insights they need, exactly when they need them.

Final Thoughts

An accounts receivable aging report is a non-negotiable tool for managing the financial well-being of your business. While conventional BI tools like Looker offer a path to create these reports, the process often depends on technical teams to set up and maintain the underlying logic, which can create delays and limit flexibility for the people who need the data most.

We built Graphed to completely remove that friction. Instead of navigating complex interfaces or waiting in line for a developer, you connect your accounting data (like QuickBooks or Stripe) and simply describe the report you want in plain English. Graphed automatically builds an interactive, live-updating dashboard for you in seconds, letting you and your team get immediate answers about your cash flow, overdue accounts, and overall financial health just by asking questions.