Accounts Receivable and Aging Analysis
This dataset provides a comprehensive view of accounts receivable balances, aging buckets, collection rates, bad debt, write-offs, and AR days, enabling organizations to monitor financial performance and optimize collection strategies. It supports granular analysis by payer and invoice, facilitating prioritization of collection efforts and risk assessment for outstanding receivables.
Sample rows
preview · 8 of 100 rows · all 16 columns| ar_record_idstring | aging_bucketstring | collection_ratefloat | payer_typestring | payer_idstring | payer_namestring | invoice_idstring | invoice_datedate | due_datedate | ar_balancefloat | bad_debt_amountfloat | write_off_amountfloat | ar_daysinteger | payment_receivedfloat | last_payment_datedate | collection_priorityinteger |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AR10001 | 1-30 | 92.5 | insurance | P001 | Springfield Health Insurance | INV-A-001 | 2023-10-12 | 2023-11-11 | 2543.78 | 0 | 0 | 20 | 2350 | 2023-11-02 | 2 |
| AR10002 | Current | 100 | customer | P002 | Green Valley Medical | INV-A-002 | 2024-01-02 | 2024-02-01 | 1200 | 0 | 0 | 10 | 1200 | 2024-02-01 | 5 |
| AR10003 | 61-90 | 67.3 | insurance | P003 | MetroCare Partners | INV-A-003 | 2023-09-30 | 2023-10-30 | 3500.45 | 1150 | 300 | 75 | 2050.45 | 2023-12-10 | 1 |
| AR10004 | 91+ | 22 | government | P004 | Central City Government | INV-G-001 | 2022-12-23 | 2023-01-22 | 4810 | 2100 | 800 | 410 | 1910 | 2023-08-01 | 1 |
| AR10005 | Current | 98 | customer | P005 | Northside Pharmacy | INV-C-001 | 2024-03-15 | 2024-04-14 | 650 | 0 | 0 | 8 | 650 | 2024-04-15 | 5 |
| AR10006 | 31-60 | 80.5 | customer | P006 | BlueStar Corporate | INV-C-002 | 2024-01-05 | 2024-02-04 | 7800 | 850 | 300 | 60 | 6650 | 2024-03-01 | 3 |
| AR10007 | 91+ | 10 | government | P007 | Federal Health Agency | INV-G-002 | 2023-06-01 | 2023-06-30 | 5670 | 4000 | 1200 | 300 | 470 | 2023-09-01 | 1 |
| AR10008 | 31-60 | 85.7 | insurance | P008 | Sunrise Insurance Group | INV-I-001 | 2023-12-01 | 2023-12-31 | 3200 | 300 | 150 | 45 | 2750 | 2024-01-15 | 3 |
| AR10009 | Current | 99.5 | customer | P009 | Riverdale Clinic | INV-C-003 | 2024-04-01 | 2024-05-01 | 440 | 0 | 0 | 5 | 440 | 2024-05-02 | 5 |
| AR10010 | 1-30 | 95 | insurance | P010 | Wellspring Mutual | INV-I-002 | 2024-02-20 | 2024-03-21 | 2100 | 0 | 0 | 22 | 1995 | 2024-04-02 | 2 |
| AR10011 | 61-90 | 64 | customer | P011 | Evergreen Medical | INV-C-004 | 2023-11-15 | 2023-12-15 | 375 | 120 | 15 | 85 | 240 | 2024-02-05 | 2 |
| AR10012 | Current | 97.3 | customer | P012 | Acme Corp. | INV-C-005 | 2024-03-20 | 2024-04-19 | 2200 | 0 | 0 | 13 | 2200 | 2024-04-20 | 4 |
| AR10013 | 91+ | 28.8 | insurance | P013 | HealthNet Insurance | INV-I-003 | 2023-08-10 | 2023-09-09 | 5740 | 3400 | 1000 | 220 | 1340 | 2023-12-21 | 1 |
| AR10014 | 1-30 | 91.6 | other | P014 | HealthFirst Foundation | INV-O-001 | 2024-01-25 | 2024-02-24 | 1950 | 0 | 0 | 21 | 1787 | 2024-03-15 | 2 |
| AR10015 | 91+ | 12 | customer | P015 | Urban Family Clinic | INV-C-006 | 2023-07-24 | 2023-08-23 | 850 | 600 | 200 | 260 | 50 | 2023-10-15 | 1 |
| AR10016 | 31-60 | 82.1 | insurance | P016 | CarePoint Insurance | INV-I-004 | 2024-02-12 | 2024-03-13 | 4060 | 500 | 235 | 37 | 3325 | 2024-03-25 | 3 |
| AR10017 | 1-30 | 89 | customer | P017 | Westside Medical Center | INV-C-007 | 2023-12-03 | 2024-01-02 | 1150 | 0 | 0 | 28 | 1023.5 | 2024-01-20 | 2 |
| AR10018 | 61-90 | 72.4 | insurance | P018 | Red Oak Insurance | INV-I-005 | 2023-11-01 | 2023-12-01 | 2950 | 500 | 300 | 76 | 2150 | 2024-01-22 | 2 |
| AR10019 | Current | 99 | other | P019 | Global Pharma | INV-O-002 | 2024-03-06 | 2024-04-05 | 4100 | 0 | 0 | 18 | 4100 | 2024-04-06 | 4 |
| AR10020 | 1-30 | 94.6 | customer | P020 | Pinnacle Health Group | INV-C-008 | 2024-01-14 | 2024-02-13 | 1390 | 0 | 0 | 27 | 1315 | 2024-02-27 | 2 |
What the 100 rows show
from the 100-row sample91+ (aging bucket) stands out: mean collection_
- 75.3median collection_
rate - 1,699median ar_
balance - 0median bad_
debt_ amount - 0.0median write_
off_ amount - 45median ar_
days - 1,189median payment_
received
Median 75.3, from 3.5 to 100.0.
- string 6
- integer 2
- float 5
- date 3
Columns
16 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 6 columns | |||
ar_record_id | string | Unique identifier for each accounts receivable recordunique | AR10001 |
payer_id | string | Unique identifier for the payer (customer, insurance, or other entity) | P001 |
payer_name | string | Name of the payer (customer, insurance, or other entity) | Green Valley Medical |
payer_type | string | Type of payer (e.g., customer, insurance, government)customer · insurance · government · other | insurance |
invoice_id | string | Unique identifier for the invoice associated with the AR record | INV-A-001 |
aging_bucket | string | Aging bucket categorizing the AR balance by days overdue (e.g., Current, 1-30, 31-60, 61-90, 91+)Current · 1-30 · 31-60 · 61-90 · 91+ | 1-30 |
| Numbers 7 columns | |||
ar_balance | float | Outstanding accounts receivable balance for the invoice0 or more | 2543.78 |
collection_rate | float | Percentage of AR collected for this payer or invoice (0-100)0 to 100 · optional | 92.5 |
bad_debt_amount | float | Amount considered as bad debt for this AR record0 or more · optional | 0 |
write_off_amount | float | Amount written off for this AR record0 or more · optional | 0 |
ar_days | integer | Number of days the AR has been outstanding (invoice_date to current date)0 or more | 20 |
payment_received | float | Total payment received against this invoice0 or more · optional | 2350 |
collection_priority | integer | Priority ranking for collection efforts (1=highest priority)1 or more · optional | 2 |
| Dates and times 3 columns | |||
invoice_date | date | Date the invoice was issued | 2023-10-12 |
due_date | date | Date payment for the invoice is due | 2023-11-11 |
last_payment_date | date | Date of the most recent payment received for this invoiceoptional | 2023-11-02 |
Use it for
A finance dashboard
Collection_
rate by aging_ bucket and a breakdown of payer_ type. Excel, Power BI or Tableau. Why do the 20 91+ rows have a mean collection_
rate of 17.2? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Ar records100AR1000192.51-30AR10002100CurrentAR1000367.361-90
A software demo
Believable ar records with payer_
id, payer_ name and payer_ type to fill a screen in front of a buyer.
This dataset100 rows16 columns
Yours10,000 rows16 columns
blueprint · accounts-receivable-and-aging-analysis
Behind this dataset
Same schema. As many rows as you need.
These 100 rows came out of a blueprint — 16 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- AR balance by payer category: commercial, Medicare, Medicaid, patient
- Aging buckets: 0-30, 31-60, 61-90, 91-120, 120+ days
- Days in AR: AR balance / (annual revenue / 365)
- Collection rate: collections / (collections + AR)
- Bad debt threshold (typically 120+ days)
- Write-off policies and approval levels
- Small balance write-offs (under $10-25)
- Collection agency referral criteria
Open the blueprint in Data Factory
1 credit per row. New accounts start with 25 free credits.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- accounts-receivable-and-aging-analysis