Finance Payment Timing Optimization

This dataset provides granular records of customer payment timing for invoices, including payment delays, overdue status, and payment methods. It enables financial institutions and commercial organizations to optimize accounts receivable processes, forecast cash flow, and identify patterns in customer payment behavior for improved risk management and operational efficiency.

  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

AR process optimization and automation

Sample rows

preview · 8 of 200 rows · all 22 columns
customer_idstringpayment_statusstringoverdue_daysintegerindustrystringcustomer_namestringcustomer_typestringinvoice_idstringinvoice_datedateinvoice_due_datedateinvoice_amountfloatcurrencystringpayment_idstringpayment_datedatepayment_amountfloatpayment_methodstringdays_to_payintegercollector_idstringcustomer_street_addressstringcustomer_citystringcustomer_statestringcustomer_postal_codestringcustomer_countrystring
C-00001paid0blankJames W. SullivanindividualINV-20240012024-04-122024-05-12725.5USDPAY-000012024-05-10725.5credit_card28COL-01480 Maplewood DrOaklandCA94607USA
C-00002overdue10Environmental ServicesSunrise Environmental Ltd.companyINV-20240022024-03-212024-04-2094500GBPPAY-000022024-04-3094500bank_transfer40COL-0344 GreenwayLondonblankW1U 5DHUK
C-00003paid0blankAnais GiraudindividualINV-20240032024-05-012024-06-01230.75EURPAY-000032024-05-27230.75cash26COL-0591 Rue de LyonParisblank75012France
C-00004overdue13ManufacturingMidwest Manufacturing Inc.companyINV-20240042024-02-182024-03-19178000USDPAY-000042024-04-01178000bank_transfer43COL-022338 Industrial CtCincinnatiOH45215USA
C-00005partial0blankMartha E. LeeindividualINV-20240052024-05-162024-06-121500USDPAY-000052024-06-101000credit_card25COL-05273 Spring StAtlantaGA30303USA
C-00006pendingblankUtilitiesGlobal Utilities GroupcompanyINV-20240062024-04-022024-05-01610000BRLblankblankblankblankblankCOL-06Av. Paulista 1009São Pauloblank01311-100Brazil
C-00007overdue3blankSanjay KumarindividualINV-20240072024-03-282024-04-251200INRPAY-000062024-04-281200bank_transfer31COL-033/14 Nehru NagarDelhiDL110065India
C-00008partial5Food & BeverageCrestview Food & BeveragecompanyINV-20240082024-04-102024-05-1053000USDPAY-000072024-05-1540000bank_transfer35COL-041290 Main StHoustonTX77002USA

What the 200 rows show

from the 200-row sample

Paid (payment status) stands out: mean overdue_days is 0.24, against 9.4 for the rest.

  • 4median overdue_days
  • 2customer types
  • 5payment methods
  • 6collectors
  • 9currencies
  • 12customer countries
Mean overdue_days by payment_status159 rows
010200.24paid67 rows9.1overdue80 rows11.3partial12 rows
overdue_days159 rows, in bands of 5
045908541171040202035overdue_days →

Median 4, from 0 to 35.

industry106 rows with a value · 94 left blank
  1. Consulting16
  2. Manufacturing14
  3. Utilities12
  4. Education12
  5. Software11
  6. Energy10
  7. Technology10
  8. Finance8
  9. Food & Beverage7
  10. Environmental Services6
22 columns by typefrom the column list below
  • string 15
  • integer 2
  • float 2
  • date 3

Columns

22 columns in three groups
blueprint · 22 columns
columntypedescriptionexample
Text 15 columns
customer_idstringUnique identifier for each customeruniqueC-00001
customer_namestringFull name of the customer or companyJames W. Sullivan
customer_typestringType of customer (e.g., individual, company)individual · companyindividual
industrystringIndustry sector of the customer10 industries · optionalEnvironmental Services
invoice_idstringUnique identifier for each invoice issued to the customeruniqueINV-2024001
currencystringCurrency code for the invoice amount (ISO 4217)9 currenciesUSD
payment_idstringUnique identifier for each payment transactionunique · optionalPAY-00001
payment_methodstringMethod used for payment (e.g., bank_transfer, credit_card, check)bank_transfer · credit_card · check · cash · other · optionalcredit_card
payment_statusstringStatus of the payment (e.g., paid, overdue, partial, pending)paid · overdue · partial · pendingpaid
collector_idstringIdentifier for the AR collector or team responsible for the account6 collectors · optionalCOL-01
customer_street_addressstringStreet address of the customeroptional480 Maplewood Dr
customer_citystringCity of the customeroptionalOakland
customer_statestringState or province of the customeroptionalCA
customer_postal_codestringPostal code of the customeroptional94607
customer_countrystringCountry of the customer12 countries · optionalUSA
Numbers 4 columns
invoice_amountfloatTotal amount billed in the invoice0 or more725.5
payment_amountfloatAmount paid by the customer for the invoice0 or more · optional725.5
days_to_payintegerNumber of days between invoice date and payment date0 or more · optional28
overdue_daysintegerNumber of days payment was overdue (if applicable)0 or more · optional0
Dates and times 3 columns
invoice_datedateDate when the invoice was issued2024-04-12
invoice_due_datedateDate when the invoice payment is due2024-05-12
payment_datedateDate when the payment was receivedoptional2024-05-10

Use it for

  • median overdue…4159 rowsmean overdue days by …0.24paid9.1overdue11.3partial

    A finance dashboard

    Overdue_days by payment_status and a breakdown of industry. Excel, Power BI or Tableau.

  • Why do the 67 paid rows have a mean overdue_days of 0.24?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable customers with customer_name, customer_type and industry to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 22 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows22 columns
Yours1,000 rows22 columnscustomer_street_address: UK only

blueprint · finance-payment-timing-optimization

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 22 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
  • Track payment dates per invoice
  • Segment by payment method
  • Flag late and early payments
  • Include customer segment data
Rows
Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
finance-payment-timing-optimization

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