Finance
24 columns
100 rows
8 formats

Customer Payment History and Credit Scoring

This dataset provides a detailed record of subscriber payment transactions, invoice histories, payment methods, late payment incidents, collections status, deposit requirements, and credit scores for postpaid eligibility. It enables comprehensive analysis of customer financial behavior, risk assessment, and credit management for telecommunications or finance providers.

opened 8 times
last updated 1 Nov 2025
by GoMask

The brief that made it

Credit risk assessment and postpaid eligibility analysis

Sample rows

preview · 8 of 100 rows · all 24 columns

customer_idstring	collection_statusstring	invoice_amountfloat	is_late_paymentboolean	address_countrystring	full_namestring	emailstring	phone_numberstring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	invoice_idstring	invoice_datedate	invoice_due_datedate	payment_idstring	payment_datedatetime	payment_amountfloat	payment_methodstring	late_payment_daysinteger	deposit_requiredboolean	credit_scoreinteger	postpaid_eligibleboolean
CUST0012A	none	109.99	false	USA	Jessica Chen	jessica.chen87@mail.com	+14159273012	152 Market St	San Francisco	CA	94105	INV0001A	2024-04-04	2024-04-19	PAY0001A	2024-04-18T14:23:00	109.99	credit_card	0	false	802	true
CUST0029B	none	67.5	true	UK	Mohammed Iqbal	miqbal23@webmail.net	+447965008393	34 King St	London	London	EC2V 8EH	INV0002B	2024-03-11	2024-03-26	PAY0002B	2024-03-29T11:43:00	67.5	bank_transfer	3	false	710	true
CUST0036C	none	120	false	Australia	Helen G. Alvarez	helen.alvarez@alvarez.org	+61234567890	21 Oxford Rd	Sydney	NSW	2000	INV0003C	2024-05-03	2024-05-18	PAY0003C	2024-05-18T09:15:00	120	auto-pay	0	false	776	true
CUST0043D	none	89.95	true	USA	Samuel Richardson	sam.richardson@gmail.com	+12025550143	88 Broadway	New York	NY	10007	INV0004D	2024-01-15	2024-01-30	PAY0004D	2024-02-08T10:33:00	89.95	credit_card	9	false	733	true
CUST0058E	none	74.5	true	Italy	Isabella Rossi	isabella.rossi@libero.it	+39069876543	Via Roma 45	Rome	RM	00184	INV0005E	2024-02-22	2024-03-09	PAY0005E	2024-03-12T13:22:00	74.5	debit_card	3	false	625	true
CUST0066F	none	100	false	Singapore	Karen Lee	karen.lee@outlook.com	+6581234567	10 Orchard Rd	Singapore	SG	238840	INV0006F	2024-04-21	2024-05-06	PAY0006F	2024-05-06T16:05:00	100	auto-pay	0	false	799	true
CUST0071G	none	79.99	true	USA	Leonard Foster	lfoster@fosternet.com	+16175551234	77 Beacon St	Boston	MA	02108	INV0007G	2024-05-28	2024-06-12	PAY0007G	2024-06-13T09:44:00	79.99	credit_card	1	false	745	true
CUST0089H	none	95.25	false	France	Chloe Dubois	c.dubois@orange.fr	+33123456789	27 Rue Lafayette	Paris	IDF	75009	INV0008H	2024-03-20	2024-04-04	PAY0008H	2024-04-04T10:00:00	95.25	bank_transfer	0	false	768	true
CUST0095I	none	110	false	UAE	Omar Siddiqui	omar.siddiqui@protonmail.com	+971501234567	13 Palm Jumeirah	Dubai	DU	00000	INV0009I	2024-02-10	2024-02-25	PAY0009I	2024-02-25T18:55:00	110	debit_card	0	false	792	true
CUST0100J	none	80	true	Sweden	Lucas Johansson	lucas.johansson@swedemail.se	+468123456	5 Vasagatan	Stockholm	STO	11120	INV0010J	2024-04-10	2024-04-25	PAY0010J	2024-04-28T08:30:00	80	credit_card	3	false	726	true
CUST0112K	none	96.75	false	India	Priya Menon	priya.menon@gmail.com	+912264009876	44 Residency Rd	Bangalore	KA	560025	INV0011K	2024-03-05	2024-03-20	PAY0011K	2024-03-19T10:44:00	96.75	auto-pay	0	false	799	true
CUST0124L	none	99.99	true	Brazil	Miguel Santos	miguel.santos@brmail.com	+5511987654321	Av Paulista 678	Sao Paulo	SP	01310-100	INV0012L	2024-05-15	2024-05-30	PAY0012L	2024-06-01T12:01:00	99.99	bank_transfer	2	false	758	true
CUST0136M	none	84	false	Ireland	Ethan McCarthy	emccarthy@irishmail.ie	+353871234567	12 Parnell Sq	Dublin	D	D01	INV0013M	2024-01-28	2024-02-12	PAY0013M	2024-02-12T08:30:00	84	auto-pay	0	false	783	true
CUST0144N	none	97.3	true	Spain	Sofia Martinez	sofia.martinez@correo.es	+34660123456	Calle Mayor 76	Madrid	MD	28013	INV0014N	2024-04-19	2024-05-04	PAY0014N	2024-05-07T15:10:00	97.3	credit_card	3	false	765	true
CUST0151O	none	88.8	false	South Korea	David Kim	david.kim@naver.com	+821012345678	23 Gangnam-daero	Seoul	KR	06236	INV0015O	2024-05-09	2024-05-24	PAY0015O	2024-05-24T21:25:00	88.8	debit_card	0	false	799	true
CUST0164P	none	105.5	true	Germany	Anna Müller	anna.mueller@web.de	+491711234567	Hauptstr. 15	Berlin	BE	10117	INV0016P	2024-03-21	2024-04-05	PAY0016P	2024-04-07T13:45:00	105.5	auto-pay	2	false	712	true
CUST0172Q	none	90	false	USA	Emily Brown	emily.brown@mail.com	+12061234567	5 Elm St	Chicago	IL	60601	INV0017Q	2024-02-11	2024-02-26	PAY0017Q	2024-02-26T17:05:00	90	credit_card	0	false	791	true
CUST0188R	none	98.5	true	Mexico	Juan Carlos	juan.carlos@correo.com.mx	+525512345678	Avenida Juarez 12	Mexico City	CDMX	06010	INV0018R	2024-03-22	2024-04-06	PAY0018R	2024-04-09T12:36:00	98.5	bank_transfer	3	false	755	true
CUST0194S	none	112	false	Canada	Linda Evans	linda.evans@evansmail.com	+16172123456	3 Maple Ave	Toronto	ON	M4E 1B2	INV0019S	2024-05-12	2024-05-27	PAY0019S	2024-05-27T11:07:00	112	auto-pay	0	false	808	true
CUST0208T	none	85	true	UAE	Fatima Hassan	fatima.hassan@emirates.net	+971501998877	99 Sheikh Zayed Rd	Dubai	DU	00000	INV0020T	2024-02-13	2024-02-28	PAY0020T	2024-03-02T09:56:00	85	debit_card	3	false	734	true

What the 100 rows show

from the 100-row sample

40%is_late_payment = true
89.3median invoice_amount
5payment methods
89.3median payment_amount
0median late_payment_days
0median deposit_amount

collection_status100 rows by value

invoice_amount100 rows, in bands of 20

Median 89.3, from 54.7 to 220.2.

address_country100 rows · top 10 of 25 values

USA22
UK6
France6
Russia6
UAE5
Spain5
Germany5
Mexico5
Italy4
Sweden4

24 columns by typefrom the column list below

string 13
integer 2
float 3
date 2
datetime 1
boolean 3

Columns

24 columns in four groups

blueprint · 24 columns
column	type	description	example
Text 13 columns
`customer_id`	string	Unique identifier for each subscriber/customerunique	CUST0012A
`full_name`	string	Full legal name of the customer	Jessica Chen
`email`	string	Customer's email addressunique	jessica.chen87@mail.com
`phone_number`	string	Customer's primary contact phone numberoptional	+14159273012
`address_street`	string	Customer's street addressoptional	152 Market St
`address_city`	string	Customer's cityoptional	San Francisco
`address_state`	string	Customer's state or provinceoptional	CA
`address_postal_code`	string	Customer's postal or ZIP codeoptional	94105
`address_country`	string	Customer's countryoptional	USA
`invoice_id`	string	Unique identifier for each invoiceunique	INV0001A
`payment_id`	string	Unique identifier for each payment transactionunique	PAY0001A
`payment_method`	string	Method used for payment (auto-pay, credit card, bank transfer, etc.)6 values	credit_card
`collection_status`	string	Current status regarding collections (none, in_collection, resolved, written_off)none · in_collection · resolved · written_off	none
Numbers 5 columns
`invoice_amount`	float	Total amount billed in the invoice0 or more	109.99
`payment_amount`	float	Amount paid in the transaction0 or more	109.99
`late_payment_days`	integer	Number of days payment was late (0 if on time)0 or more · optional	0
`deposit_amount`	float	Amount of security deposit required (0 if not required)0 or more · optional	0
`credit_score`	integer	Customer's credit score used for postpaid eligibility300 to 850	802
Dates and times 3 columns
`invoice_date`	date	Date the invoice was issued	2024-04-04
`invoice_due_date`	date	Date the invoice payment is due	2024-04-19
`payment_date`	datetime	Date and time the payment was made	2024-04-18T14:23:00
True or false 3 columns
`is_late_payment`	boolean	Indicates if the payment was late	false
`deposit_required`	boolean	Indicates if a security deposit is required for the account	false
`postpaid_eligible`	boolean	Indicates if the customer is eligible for postpaid services based on credit score and payment history	true

Use it for

A finance dashboard
The is_late_payment rate, invoice_amount by collection_status and a breakdown of address_country. Excel, Power BI or Tableau.
Why do 40 of 100 rows have is_late_payment = true?
A class exercise
Hand out the rows and one question. Everyone works from the same 100 rows.
Customers100
CUST0012A109.99none
CUST0029B67.5none
CUST0043D89.95none
A software demo
Believable customers with full_name, email and phone_number to fill a screen in front of a buyer.

This dataset100 rows24 columns

Yours10,000 rows24 columnsaddress_street: UK only

blueprint · customer-payment-history-and-credit-scoring

Behind this dataset

Same schema. As many rows as you need.

These 100 rows came out of a blueprint — 24 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

Payment transaction ID and invoice number
Subscriber MSISDN and billing account
Invoice amount, due date, and payment date
Payment status: paid on time, late, partial, unpaid
Payment method: credit card, bank ACH, mobile wallet, cash, voucher
Auto-pay enrollment status for convenience and on-time payment
Late payment incidents count and days overdue
Collections status: reminder sent, suspended service, collections agency

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

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: customer-payment-history-and-credit-scoring

What should your data show?

No signup. No card.

Sample rows

What the 100 rows show

Columns

Use it for

A finance dashboard

A class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Finance

What should your data show?