b2KIT

Accounts Receivable Aging Report Builder

Build AR aging reports with 30/60/90/120+ day buckets, collection probability, bad debt estimate, and customer concentration analysis.

Tested tool guide Tested browser tools Checked August 16, 2026

What Accounts Receivable Aging Report Builder does, with a checked example

This tool takes a list of open invoices (customer, invoice number, amount, due date), buckets each by how many days it is past a chosen report date, then rolls totals up by bucket and by customer. It applies a collection-probability weight to each bucket to produce an expected-collectible figure and a bad debt reserve estimate, and separately reports what share of total AR sits with each customer. The most common mistake is entering the invoice date instead of the due date, or forgetting payment terms (net-30, net-60), which silently shifts every invoice into the wrong bucket and skews the whole report.

Worked example

A concrete input and expected output from the current implementation.

Input

Report date 2026-08-16. Invoices: Customer A #1001 $5,000 due 2026-07-01; Customer A #1002 $3,000 due 2026-05-01; Customer B #2001 $2,000 due 2026-08-10. Collection probability by bucket: 1-30 days = 95%, 31-60 days = 85%, 91-120 days = 50%.

Expected output

Aging: 1-30 days $2,000 (#2001, 6 days past due); 31-60 days $5,000 (#1001, 46 days past due); 61-90 days $0; 91-120 days $3,000 (#1002, 107 days past due); 120+ $0. Total AR $10,000. Expected collectible = (2000x0.95)+(5000x0.85)+(3000x0.50) = $7,650. Bad debt estimate = $2,350. Concentration: Customer A $8,000 (80%), Customer B $2,000 (20%).

Days past due is computed as report date minus due date for each invoice, which sorts #2001 into the 1-30 bucket, #1001 into 31-60, and #1002 into 91-120; the bad debt figure is total AR minus the sum of each bucket's balance times its supplied collection probability.

How the result is produced

1

Per-invoice bucketing

For each invoice the tool subtracts the due date from the report date to get days past due, then sorts the balance into current, 1-30, 31-60, 61-90, 91-120, or 120+ buckets. Bucket totals are summed both overall and per customer, so the same invoice list drives both the aging table and the concentration breakdown.

2

Weighted bad debt and concentration

Each bucket total is multiplied by a collection-probability percentage (editable, since older buckets are normally less collectible) and summed to get expected collectible cash; total AR minus that figure is the bad debt estimate. Separately, each customer's invoice total is divided by total AR to flag concentration when one customer dominates the balance.

Good uses

  • A controller compiling a month-end AR aging schedule for the CFO or an auditor
  • Estimating an allowance-for-doubtful-accounts entry before closing the books
  • Checking whether one or two customers account for an outsized share of receivables before extending them more credit

Limits and checks

  • The bucket a balance lands in is only as accurate as the due date entered; using invoice date, or ignoring net-30/net-60 terms, shifts every invoice into the wrong bucket without any warning
  • Default collection probabilities are generic placeholders, not derived from this company's actual collection history, so the bad debt figure should be treated as a rough estimate until the rates are replaced with real historical recovery rates by bucket
  • The tool ages whatever outstanding balance is entered per invoice; it does not track partial payments, credits, or disputes itself, so those need to be netted into the balance before entry or the aging will overstate what is actually owed

Common questions

Does it connect to QuickBooks or my accounting system to pull invoices automatically?

No. This is a standalone calculator - you enter or paste invoice data by hand (customer, amount, due date). It does not import from or sync with any accounting or ERP system, so the report is only as current as the data you last entered.

Are the default collection probabilities and bad debt percentages accurate for my business?

No, treat them as generic starting assumptions, not a verified benchmark. Collection rates vary widely by industry, customer base, and economic conditions, so replace the defaults with your own historical write-off and recovery rates by bucket before relying on the bad debt estimate for accounting entries.

References and verification

The example and behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.

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