Finance
30 columns
200 rows
8 formats

Credit Default Early Warning

This dataset provides a comprehensive view of customer loan histories, payment behaviors, and annotated default events, enabling financial institutions to proactively identify and manage potential credit risks. It includes detailed customer profiles, loan attributes, payment records, and risk indicators, making it ideal for predictive analytics and early warning systems.

last updated 20 Aug 2025
by GoMask

The brief that made it

Early warning and prediction of loan defaults

Sample rows

preview · 8 of 200 rows · all 30 columns

customer_idstring	payment_statusstring	days_past_dueinteger	default_flagboolean	loan_typestring	first_namestring	last_namestring	date_of_birthdate	genderstring	marital_statusstring	employment_statusstring	annual_incomefloat	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	loan_idstring	loan_amountfloat	loan_issue_datedate	loan_due_datedate	interest_ratefloat	payment_idstring	payment_datedate	payment_amountfloat	default_datedate	default_reasonstring	credit_scoreinteger	risk_levelstring	last_updateddatetime
CUST001	on_time	0	false	auto	Ava	Miller	1993-05-16	female	single	employed	78300	18 Willow Lane	Seattle	WA	98104	US	LN10001	18500	2022-11-19	2027-11-19	6.7	PMT50001	2024-03-01	370	blank	blank	743	low	2024-03-02T10:29:11
CUST002	on_time	0	false	personal	Zachary	Nguyen	1968-11-21	male	married	retired	23200	57 Oakway Ave	Vancouver	BC	V6B2L4	Canada	LN10002	7200	2021-06-15	2024-06-15	11.2	PMT50002	2024-02-24	400	blank	blank	655	medium	2024-02-25T08:43:55
CUST003	partial	4	false	student	Maya	Petrov	2001-09-07	female	single	student	0	3449 College Dr	Oxford	Oxfordshire	OX1 2JD	UK	LN10003	8200	2023-09-01	2026-09-01	3.5	PMT50003	2024-03-04	0	blank	blank	699	medium	2024-03-05T18:10:24
CUST004	on_time	0	false	business	Liam	O'Donnell	1981-02-14	male	married	self-employed	134500	2223 Dublin Rd	Dublin	Leinster	D02YK01	Ireland	LN10004	43000	2021-10-30	2026-10-30	9.1	PMT50004	2024-01-29	1300	blank	blank	732	low	2024-01-31T12:17:34
CUST005	on_time	0	false	credit_card	Fatima	Alvi	1997-07-20	female	single	employed	51200	11 Jumeirah St	Dubai	Dubai	00000	UAE	LN10005	2400	2022-08-28	2026-08-28	17.4	PMT50005	2024-02-10	150	blank	blank	721	low	2024-02-11T19:01:46
CUST006	late	7	false	personal	Ethan	Brown	1958-07-03	male	widowed	retired	18900	800 Sunset Blvd	Los Angeles	CA	90046	US	LN10006	3700	2020-09-11	2025-09-11	13.2	PMT50006	2023-12-18	200	blank	blank	634	medium	2023-12-19T06:39:57
CUST007	on_time	0	false	auto	Minh	Tran	1990-03-27	male	single	employed	95400	91 Nguyen St	Ho Chi Minh City	HCMC	700000	Vietnam	LN10007	15600	2022-04-03	2027-04-03	5.9	PMT50007	2024-02-19	390	blank	blank	771	low	2024-02-20T14:52:28
CUST008	on_time	0	false	business	Sophie	Dubois	1976-11-10	female	married	self-employed	112600	8 Rue Lafayette	Paris	Île-de-France	75009	France	LN10008	70500	2020-03-22	2025-03-22	10.4	PMT50008	2024-01-10	1900	blank	blank	746	low	2024-01-12T13:05:33
CUST009	on_time	0	false	mortgage	Carlos	Lopez	1989-02-08	male	single	employed	78200	57 Avenida Juarez	Mexico City	CDMX	01000	Mexico	LN10009	355400	2019-10-01	2034-10-01	3.2	PMT50009	2024-02-07	2105	blank	blank	775	low	2024-02-09T17:50:14
CUST010	partial	3	false	student	Anya	Volkov	2002-06-21	female	single	student	0	512 Pushkin St	St Petersburg	SPB	190000	Russia	LN10010	5600	2023-08-30	2026-08-30	2.1	PMT50010	2024-03-05	0	blank	blank	711	low	2024-03-06T15:21:19
CUST011	late	6	false	auto	Jasper	Smith	1984-09-18	male	divorced	employed	64200	88 Queen St	Brisbane	QLD	4000	Australia	LN10011	21300	2021-04-18	2026-04-18	7.5	PMT50011	2024-02-15	410	blank	blank	682	medium	2024-02-16T11:37:15
CUST012	on_time	0	false	credit_card	Isabella	Chen	1998-03-04	female	single	employed	55200	14 Orchard Rd	Singapore	SG	238838	Singapore	LN10012	3300	2022-11-24	2027-11-24	19.1	PMT50012	2024-03-01	195	blank	blank	725	low	2024-03-02T08:24:03
CUST013	late	8	false	personal	Alexis	Johnson	1951-01-13	female	widowed	retired	21900	1345 Lakeview Dr	Minneapolis	MN	55401	US	LN10013	4900	2020-03-12	2025-03-12	13.6	PMT50013	2023-12-15	250	blank	blank	626	medium	2023-12-16T13:18:55
CUST014	on_time	0	false	business	Olga	Kuznetsova	1973-08-15	female	married	self-employed	101200	77 Tverskaya St	Moscow	Moscow	125009	Russia	LN10014	51900	2021-10-13	2026-10-13	10.7	PMT50014	2024-02-22	1650	blank	blank	738	low	2024-02-23T07:36:30
CUST015	on_time	0	false	mortgage	Samuel	Park	1986-04-29	male	single	employed	72500	29 Itaewon Rd	Seoul	Seoul	04348	South Korea	LN10015	420000	2020-12-01	2035-12-01	3.8	PMT50015	2024-01-30	2200	blank	blank	791	low	2024-01-31T09:59:21
CUST016	partial	5	false	student	Priya	Singh	1999-09-25	female	single	student	0	202 MG Road	Pune	MH	411001	India	LN10016	12000	2023-09-20	2026-09-20	3.2	PMT50016	2024-03-03	0	blank	blank	682	medium	2024-03-04T20:05:42
CUST017	missed	37	true	personal	Benjamin	Garcia	1961-12-04	male	divorced	retired	16300	11 Calle Mayor	Madrid	Madrid	28013	Spain	LN10017	5100	2020-02-16	2025-02-16	12.7	PMT50017	2023-12-13	250	2024-01-15	Prolonged missed payment period	514	high	2024-01-17T15:43:20
CUST018	on_time	0	false	personal	Emily	Clark	1996-02-23	female	single	employed	66500	54 King St	London	London	SW1A 1AA	UK	LN10018	6200	2022-06-22	2025-06-22	13.9	PMT50018	2024-02-18	340	blank	blank	701	low	2024-02-19T09:16:02
CUST019	on_time	0	false	mortgage	Mohammed	Saleh	1987-08-31	male	married	employed	90800	88 Sheikh Zayed Rd	Dubai	Dubai	00000	UAE	LN10019	367000	2021-05-10	2036-05-10	4.5	PMT50019	2024-01-28	1980	blank	blank	778	low	2024-01-29T07:33:41
CUST020	on_time	0	false	business	Lucia	Rossi	1979-10-11	female	married	self-employed	129400	17 Via Roma	Rome	Lazio	00186	Italy	LN10020	62600	2020-08-09	2025-08-09	9.7	PMT50020	2024-01-12	1800	blank	blank	751	low	2024-01-13T08:32:14

What the 200 rows show

from the 200-row sample

Missed (payment status) stands out: mean days_past_due is 42.6, against 2.3 for the rest.

9%default_flag = true
0median days_past_due
4genders
4risk levels
5marital statuses
6employment statuses

Mean days_past_due by payment_status200 rows

days_past_due200 rows, in bands of 10

Every row with days_past_due of 30 or more has default_flag = true. None below it.

loan_type200 rows · 7 values

personal63
auto39
mortgage32
student25
business23
credit_card17
other1

30 columns by typefrom the column list below

string 17
integer 2
float 4
date 5
datetime 1
boolean 1

Columns

30 columns in four groups

blueprint · 30 columns
column	type	description	example
Text 17 columns
`customer_id`	string	Unique identifier for each customerunique	CUST001
`first_name`	string	Customer's first name	Ava
`last_name`	string	Customer's last name	Miller
`gender`	string	Customer's gendermale · female · other · prefer_not_to_say · optional	female
`marital_status`	string	Customer's marital statussingle · married · divorced · widowed · other · optional	single
`employment_status`	string	Customer's employment status6 values · optional	employed
`address_street`	string	Customer's street addressoptional	18 Willow Lane
`address_city`	string	Customer's city of residenceoptional	Seattle
`address_state`	string	Customer's state or province of residenceoptional	WA
`address_postal_code`	string	Customer's postal codeoptional	98104
`address_country`	string	Customer's country of residenceoptional	US
`loan_id`	string	Unique identifier for each loanunique	LN10001
`loan_type`	string	Type of loan issued to the customer7 values	auto
`payment_id`	string	Unique identifier for each payment transactionunique	PMT50001
`payment_status`	string	Status of the payment (on-time, late, missed)on_time · late · missed · partial	on_time
`default_reason`	string	Reason for default (if applicable)optional	Repeated missed payments
`risk_level`	string	Risk level assigned to the customer at loan issuancelow · medium · high · critical · optional	low
Numbers 6 columns
`annual_income`	float	Customer's annual income in local currency0 or more · optional	78300
`loan_amount`	float	Original principal amount of the loan0 or more	18500
`interest_rate`	float	Annual interest rate applied to the loan (percentage)0 to 100	6.7
`payment_amount`	float	Amount paid in the transaction0 or more	370
`days_past_due`	integer	Number of days payment was overdue (0 if on time)0 or more · optional	0
`credit_score`	integer	Customer's credit score at time of loan issuance300 to 850 · optional	743
Dates and times 6 columns
`date_of_birth`	date	Customer's date of birth	1993-05-16
`loan_issue_date`	date	Date when the loan was issued	2022-11-19
`loan_due_date`	date	Scheduled date for loan repayment completion	2027-11-19
`payment_date`	date	Date when the payment was made	2024-03-01
`default_date`	date	Date when the loan was flagged as default (if applicable)optional	2024-01-15
`last_updated`	datetime	Timestamp of last record update	2024-03-02T10:29:11
True or false 1 column
`default_flag`	boolean	Indicates whether the loan is in default (true/false)	false

Use it for

A finance dashboard
The default_flag rate, days_past_due by payment_status and a breakdown of loan_type. Excel, Power BI or Tableau.
Why do the 18 missed rows have a mean days_past_due of 42.6?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Customers200
CUST0010on_time
CUST01737missed
CUST02941missed
A software demo
Believable customers with first_name, last_name and date_of_birth to fill a screen in front of a buyer.

This dataset200 rows30 columns

Yours1,000 rows30 columnsaddress_street: UK only

blueprint · credit-default-early-warning

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 30 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

Contains customer demographics and credit history
Monthly payment records tracked
Default status labeled annually
Simulated for privacy and compliance

Rows1,000 rows10,000 rows100,000 rows1,000,000 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: credit-default-early-warning

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

A finance dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Finance

What should your data show?