B2B Invoice Payment Patterns

This dataset provides detailed B2B invoice payment histories, including invoice amounts, payment delays, business entity profiles, and payment terms. It enables financial planning, cash flow optimization, and risk assessment by revealing payment patterns and behaviors across industries and geographies.

  • opened 5 times
  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Cash flow forecasting and optimization

Sample rows

preview · 8 of 200 rows · all 26 columns
invoice_idstringpayment_statusstringinvoice_amountfloatdiscount_appliedbooleanindustry_sectorstringseller_idstringbuyer_idstringinvoice_datedatedue_datedatepayment_datedatecurrencystringpayment_delay_daysintegerlate_fee_appliedbooleanseller_namestringseller_street_addressstringseller_citystringseller_statestringseller_postal_codestringseller_countrystringbuyer_namestringbuyer_street_addressstringbuyer_citystringbuyer_statestringbuyer_postal_codestringbuyer_countrystringinvoice_termsstring
INV-00001paid1423.55trueRetailSELLER0021BUYER10012023-01-052023-02-042023-02-01USD0falseBayshore Retail LLC950 Market StSan FranciscoCA94103USAUrban Solutions Inc.21 Broadway AveAustinTX78701USANet 30
INV-00002overdue2739.1falseServicesSELLER0022BUYER10022023-01-102023-01-242023-02-02USD9trueSilverleaf Services LLC520 Oak DrDallasTX75202USAMidtown Group PLC88 Lincoln StChicagoIL60601USANet 14
INV-00003unpaid4998falseManufacturingSELLER0023BUYER10032023-01-122023-02-11blankUSDblankfalseCrestview Hospitality Inc.1472 Beach AveMiamiFL33139USASunrise Manufacturing GmbH11 WerkstrasseHamburgHH20095GermanyNet 30
INV-00004overdue70012.25falseLuxury GoodsSELLER0024BUYER10042023-01-152023-01-302023-02-20EUR21trueEuroTech Solutions GmbH17 BahnhofstrasseZurichZH8001SwitzerlandPremier Luxury SA101 Rue du RhôneGenevaGE1204SwitzerlandNet 15
INV-00005paid655.75trueFoodSELLER0025BUYER10052023-01-182023-02-172023-02-17USD0falseGreenfield Food Co.302 Elm StSeattleWA98101USAFresh Eats Ltd.65 King StTorontoONM5H 2N2CanadaNet 30
INV-00006unpaid23870falseTechnologySELLER0026BUYER10062023-01-202023-02-19blankINRblankfalseIndus Services Pvt Ltd12 MG RoadMumbaiMH400001IndiaSkyline Technologies Ltd.104 Nehru PlaceDelhiDL110019IndiaNet 30
INV-00007paid98.5falseHospitalitySELLER0027BUYER10072023-01-222023-01-222023-01-22CAD0falseMaple Hospitality Ltd.812 Queen StVancouverBCV5K 0A1CanadaNorthern Events Inc.33 1st AveCalgaryABT2P 1J9CanadaDue on Receipt
INV-00008overdue1800.6falseReal EstateSELLER0028BUYER10082023-01-252023-02-242023-03-02USD6trueSunrise Entertainment LLC900 Las Vegas BlvdLas VegasNV89101USAPacific Real Estate PLC120 Ocean AveLos AngelesCA90001USANet 30

What the 200 rows show

from the 200-row sample

Paid (payment status) stands out: 45 of its 69 rows have discount_applied = true, against 2 of 131 for the rest.

  • 24%discount_applied = true
  • 1,793median invoice_amount
  • 7currencies
  • 7seller countries
  • 7invoice termses
  • 8buyer countries
Discount applied rate by payment_statusdiscount_applied = true
0%50%100%65%paid45 of 690%unpaid0 of 543%overdue2 of 77
invoice_amount200 rows, in bands of 1M
09519018582000101305M10Minvoice_amount →

Median 1,793, from 22.8 to 10,000,000.

industry_sector200 rows · top 10 of 12 values
  1. Hospitality34
  2. Food20
  3. Retail19
  4. Automotive18
  5. Services16
  6. Manufacturing16
  7. Luxury Goods14
  8. Technology14
  9. Government14
  10. Entertainment13
26 columns by typefrom the column list below
  • string 19
  • integer 1
  • float 1
  • date 3
  • boolean 2

Columns

26 columns in four groups
blueprint · 26 columns
columntypedescriptionexample
Text 19 columns
invoice_idstringUnique identifier for the invoiceuniqueINV-00001
seller_idstringUnique identifier for the selling businessSELLER0021
buyer_idstringUnique identifier for the buying businessBUYER1001
currencystringCurrency code for the invoice amount (ISO 4217)7 currenciesUSD
payment_statusstringCurrent payment status of the invoicepaid · unpaid · partial · overduepaid
seller_namestringLegal name of the selling businessBayshore Retail LLC
seller_street_addressstringStreet address of the selling businessoptional950 Market St
seller_citystringCity of the selling businessoptionalSan Francisco
seller_statestringState or province of the selling businessoptionalCA
seller_postal_codestringPostal code of the selling businessoptional94103
seller_countrystringCountry of the selling business7 countries · optionalUSA
buyer_namestringLegal name of the buying businessUrban Solutions Inc.
buyer_street_addressstringStreet address of the buying businessoptional21 Broadway Ave
buyer_citystringCity of the buying businessoptionalAustin
buyer_statestringState or province of the buying businessoptionalTX
buyer_postal_codestringPostal code of the buying businessoptional78701
buyer_countrystringCountry of the buying business8 countries · optionalUSA
industry_sectorstringIndustry sector of the buyer (e.g., Manufacturing, Retail, Services)12 sectors · optionalRetail
invoice_termsstringPayment terms for the invoice (e.g., Net 30, Net 60)7 termses · optionalNet 30
Numbers 2 columns
invoice_amountfloatTotal amount due on the invoice0 or more1423.55
payment_delay_daysintegerNumber of days payment was delayed past the due date (0 if paid on or before due date)0 or more · optional0
Dates and times 3 columns
invoice_datedateDate the invoice was issued2023-01-05
due_datedateDate payment is due for the invoice2023-02-04
payment_datedateDate payment was received for the invoiceoptional2023-02-01
True or false 2 columns
discount_appliedbooleanIndicates if any early payment discount was appliedtrue
late_fee_appliedbooleanIndicates if any late payment fee was appliedfalse

Use it for

  • discount appli…24%47 of 200 rowsmean invoice amount b…1.1kpaid917.5kunpaid290.7koverdue

    A finance dashboard

    The discount_applied rate, invoice_amount by payment_status and a breakdown of industry_sector. Excel, Power BI or Tableau.

  • Why do 47 of 200 rows have discount_applied = true?

    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 invoices with seller_id, buyer_id and invoice_date to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 26 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows26 columns
Yours1,000 rows26 columnsseller_street_address: UK only

blueprint · b2b-invoice-payment-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 26 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
  • Invoice issue and due dates recorded
  • Payment delay classified
  • Client sector mapped
  • Invoice amount provided
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
b2b-invoice-payment-patterns

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