Finance
24 columns
200 rows
8 formats

Credit Approval Application Dataset

This dataset provides detailed records of credit applications, including applicant demographics, financial background, requested loan details, application outcomes, and product types. It enables financial institutions to analyze approval rates, assess risk profiles, and optimize credit product offerings for diverse customer segments.

last updated 20 Aug 2025
by GoMask

The brief that made it

Credit risk modeling and assessment

Sample rows

preview · 8 of 200 rows · all 24 columns

application_idstring	application_statusstring	credit_scoreinteger	loan_purposestring	applicant_idstring	applicant_namestring	date_of_birthdate	genderstring	marital_statusstring	employment_statusstring	annual_incomefloat	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	application_datedatetime	loan_amount_requestedfloat	loan_term_monthsinteger	interest_rate_offeredfloat	approval_datedatetime	product_typestring	risk_assessment_scorefloat	rejection_reasonstring
APP000001	approved	782	auto	USR001	Jane M. Alvarez	1992-04-18	female	married	employed	87000.25	1023 Grandview Ave	Houston	TX	77002	USA	2022-07-13T11:23:45	42000	48	6.25	2022-07-15T15:20:09	auto_loan	0.19	blank
APP000002	rejected	402	personal	USR002	Samuel O. Lee	1961-11-09	male	widowed	retired	19000	13 St. Andrew Road	Manchester	ENG	M1 2AB	UK	2023-02-19T14:06:22	12000	24	blank	blank	personal_loan	0.87	Credit score below minimum threshold for approval.
APP000003	approved	622	education	USR003	Liam N. Zhang	1998-08-30	male	single	student	4000	57 Zhongshan Rd	Shanghai	SH	200002	CN	2021-09-21T10:42:17	3500	18	12.75	2021-09-23T14:22:49	personal_loan	0.44	blank
APP000004	approved	764	business	USR004	Fatima S. Baloch	1987-03-02	female	married	self-employed	113000	25 Shadman Block	Lahore	PB	54000	PK	2022-04-29T08:11:34	30000	60	7.89	2022-05-02T09:28:13	business_loan	0.16	blank
APP000005	rejected	338	personal	USR005	Olga V. Ivanova	1956-12-12	female	divorced	retired	9000	112 Lenina Prospekt	Moscow	MOW	101000	RU	2022-11-05T17:39:19	2000	12	blank	blank	personal_loan	0.93	Credit score below minimum threshold for approval.
APP000006	approved	704	personal	USR006	Aarav R. Menon	1993-07-14	male	single	employed	64000	42 MG Road	Bangalore	KA	560001	IN	2023-05-21T13:15:05	25000	36	7	2023-05-24T09:40:32	personal_loan	0.26	blank
APP000007	approved	792	home	USR007	Diego J. Santos	1985-01-27	male	married	employed	103400	420 Rua Augusta	São Paulo	SP	01304-000	BR	2022-10-12T12:33:41	150000	120	4.35	2022-10-14T10:10:01	mortgage	0.09	blank
APP000008	approved	485	education	USR008	Sofia P. Costa	2001-05-06	female	single	student	0	89 Avenida Brasil	Lisbon	LIS	1200-399	PT	2023-06-03T09:58:16	2200	12	14.65	2023-06-06T12:31:48	personal_loan	0.65	blank
APP000009	approved	822	business	USR009	John R. Miller	1968-04-18	male	married	self-employed	122000	1643 Cherry Lane	San Francisco	CA	94109	USA	2021-03-17T15:28:40	320000	72	5.75	2021-03-21T10:08:23	business_loan	0.13	blank
APP000010	approved	703	personal	USR010	Emily Q. Ng	1977-09-23	female	married	employed	78000	720 Orchard Blvd	Singapore	SG	238879	SG	2023-10-15T18:01:12	25000	36	7.5	2023-10-17T11:27:39	personal_loan	0.33	blank
APP000011	rejected	319	personal	USR011	Mia E. Fischer	1959-02-20	female	widowed	retired	9200	81 Karlstraße	Berlin	BE	10115	DE	2021-07-26T16:47:09	1600	6	blank	blank	personal_loan	0.92	Credit score below minimum threshold for approval.
APP000012	approved	699	auto	USR012	Maksim K. Petrov	1982-08-15	male	married	employed	67000	5 Nevsky Prospekt	St. Petersburg	SPE	191011	RU	2022-08-05T10:26:33	18500	36	8.9	2022-08-08T14:18:18	auto_loan	0.38	blank
APP000013	approved	585	personal	USR013	Lucas H. Kim	1999-01-10	male	single	student	3100	94 Dongdaemun-ro	Seoul	KR	02455	KR	2023-02-14T07:37:55	1900	12	16.2	2023-02-16T11:40:05	credit_card	0.72	blank
APP000014	approved	765	home	USR014	Maria L. Gonzalez	1980-03-11	female	married	employed	99000	17 Calle Mayor	Madrid	MD	28013	ES	2024-01-07T13:13:36	49000	84	4.9	2024-01-10T10:50:12	mortgage	0.15	blank
APP000015	approved	786	business	USR015	Tommy R. Ndhlovu	1972-06-30	male	married	self-employed	83000	23 Robson St	Harare	HA	00263	ZW	2022-02-21T14:32:19	34000	48	7.68	2022-02-24T16:23:44	business_loan	0.22	blank
APP000016	approved	731	personal	USR016	Sara G. Li	1991-12-03	female	single	employed	56000	12 Nanjing East Rd	Shanghai	SH	200001	CN	2023-08-18T09:15:05	16000	24	8.3	2023-08-20T14:44:11	personal_loan	0.21	blank
APP000017	rejected	322	other	USR017	Victor S. Brown	1955-10-21	male	divorced	retired	11000	9 Pinecrest Lane	Atlanta	GA	30309	USA	2021-06-13T11:29:55	1200	6	blank	blank	other	0.96	Credit score below minimum threshold for approval.
APP000018	approved	750	home	USR018	Elena A. Rossi	1989-02-16	female	married	employed	97000	19 Via Roma	Rome	RM	00185	IT	2023-11-27T15:51:21	41000	60	5.05	2023-11-30T11:12:03	mortgage	0.17	blank
APP000019	approved	689	auto	USR019	Oscar L. Reyes	1983-04-04	male	single	employed	66000	78 Avenida Juarez	Mexico City	CDMX	06000	MX	2022-05-18T12:12:57	22000	36	8.6	2022-05-21T14:40:56	auto_loan	0.39	blank
APP000020	approved	651	personal	USR020	Alicia M. Smith	1997-06-22	female	single	employed	52000	1564 Maple Street	Toronto	ON	M5H 2N2	CA	2023-03-08T09:21:25	13500	18	8.15	2023-03-10T15:44:50	personal_loan	0.34	blank

What the 200 rows show

from the 200-row sample

Rejected (application status) stands out: mean credit_score is 370.8, against 710.5 for the rest.

693.5median credit_score
2genders
4marital statuses
4employment statuses
6product types
55,850median annual_income

Mean credit_score by application_status200 rows

credit_score200 rows, in bands of 100

Median 693.5, from 312 to 834.

loan_purpose200 rows · 6 values

personal91
auto30
business28
home26
education20
other5

24 columns by typefrom the column list below

string 15
integer 2
float 4
date 1
datetime 2

Columns

24 columns in three groups

blueprint · 24 columns
column	type	description	example
Text 15 columns
`application_id`	string	Unique identifier for each credit applicationunique	APP000001
`applicant_id`	string	Unique identifier for the applicant	USR001
`applicant_name`	string	Full name of the applicant	Jane M. Alvarez
`gender`	string	Gender of the applicantmale · female · other · prefer_not_to_say · optional	female
`marital_status`	string	Applicant's marital statussingle · married · divorced · widowed · other · optional	married
`employment_status`	string	Current employment status of the applicantemployed · self-employed · unemployed · student · retired	employed
`address_street`	string	Street address of the applicant	1023 Grandview Ave
`address_city`	string	City of the applicant's address	Houston
`address_state`	string	State or province of the applicant's address	TX
`address_postal_code`	string	Postal code of the applicant's address	77002
`address_country`	string	Country of the applicant's address	USA
`loan_purpose`	string	Purpose of the loan requested6 values	auto
`application_status`	string	Final status of the applicationapproved · rejected · pending · withdrawn	approved
`rejection_reason`	string	Reason for rejection (if application_status is 'rejected')optional	Credit score below minimu…
`product_type`	string	Type of credit product offered6 values	auto_loan
Numbers 6 columns
`annual_income`	float	Applicant's annual income in local currency0 or more	87000.25
`credit_score`	integer	Applicant's credit score at time of application300 to 850	782
`loan_amount_requested`	float	Total loan amount requested by the applicant0 or more	42000
`loan_term_months`	integer	Requested loan term in months1 or more	48
`interest_rate_offered`	float	Interest rate offered to the applicant (annual percentage)0 or more · optional	6.25
`risk_assessment_score`	float	Internal risk assessment score assigned to the application0 to 1 · optional	0.19
Dates and times 3 columns
`date_of_birth`	date	Applicant's date of birth	1992-04-18
`application_date`	datetime	Date and time when the application was submitted	2022-07-13T11:23:45
`approval_date`	datetime	Date and time when the application was approved (if applicable)optional	2022-07-15T15:20:09

Use it for

A finance dashboard
Credit_score by application_status and a breakdown of loan_purpose. Excel, Power BI or Tableau.
Why do the 34 rejected rows have a mean credit_score of 370.8?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications200
APP000001782approved
APP000002402rejected
APP000003622approved
A software demo
Believable applications with applicant_id, applicant_name and date_of_birth to fill a screen in front of a buyer.

This dataset200 rows24 columns

Yours1,000 rows24 columnsaddress_street: UK only

blueprint · credit-approval-application-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 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

Log applicant demographics
Track application status
Include requested amount and product
Flag high-risk applications

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-approval-application-dataset

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?