Finance
25 columns
200 rows
8 formats

Credit Card Application Approvals

This dataset provides comprehensive records of credit card applications, including detailed applicant profiles, financial information, application outcomes, and decision rationales. It is ideal for developing and evaluating machine learning models for credit risk assessment, approval automation, and regulatory compliance analysis.

last updated 21 Jul 2025
by GoMask

The brief that made it

Training and validating credit risk and approval machine learning models

Sample rows

preview · 8 of 200 rows · all 25 columns

application_idstring	employment_statusstring	existing_credit_cardsinteger	income_currencystring	application_datedatetime	applicant_first_namestring	applicant_last_namestring	applicant_date_of_birthdate	applicant_emailstring	applicant_phonestring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	employer_namestring	annual_incomefloat	credit_scoreinteger	existing_loansinteger	total_monthly_debt_paymentsfloat	requested_credit_limitfloat	application_statusstring	decision_datedatetime	decision_reasonstring	reviewed_bystring
A100001	employed	2	EUR	2023-04-03T10:15:00	Lucas	Weber	1991-06-22	lucas.weber@mail.de	+49 1512 3487321	Leipziger Platz 8	Berlin	Berlin	10117	Germany	Siemens AG	79000	756	0	450	12000	approved	2023-04-04T14:31:00	good credit history	jmueller
A100002	employed	1	INR	2023-04-05T13:40:00	Priya	Reddy	1998-11-12	priya.reddy@gmail.com	+91 98456 23456	12 MG Road	Bengaluru	Karnataka	560001	India	Infosys	410000	698	1	7200	85000	approved	2023-04-06T09:12:00	stable income	system_auto
A100003	self-employed	3	MXN	2023-04-06T16:22:00	Gabriela	Martinez	1984-09-30	gabriela.martinez@yahoo.com.mx	+52 5512345678	Calle Insurgentes 120	Ciudad de México	CDMX	06600	Mexico	Creativa Studio	520000	725	2	16000	65000	approved	2023-04-09T10:08:00	good income, self-employed	asoriano
A100004	employed	2	GBP	2023-04-07T09:55:00	James	O'Neill	1977-12-01	james.oneill@outlook.com	+44 7911 123456	14 Queen St	London	England	W1D 3QF	United Kingdom	HSBC	66000	782	0	350	15000	approved	2023-04-08T12:17:00	high credit score	lthompson
A100005	employed	1	GHS	2023-04-09T15:17:00	Zara	Osei	1996-05-17	zara.osei@mail.com.gh	+233 20 123 4567	15 Osu Ave	Accra	Greater Accra	GA-107-1234	Ghana	Ghana Commercial Bank	42000	655	1	900	4000	approved	2023-04-10T11:03:00	sufficient income	system_auto
A100006	employed	2	KRW	2023-04-10T11:40:00	Min-Jun	Kim	1992-02-14	minjun.kim@naver.com	+82 10-3456-7890	23 Gangnam-daero	Seoul	Seoul	06236	South Korea	Samsung	57000000	733	1	400000	18000000	approved	2023-04-12T10:44:00	steady job, good score	kchoi
A100007	retired	1	EUR	2023-04-11T17:30:00	Sophie	Dubois	1959-10-29	sophie.dubois@orange.fr	+33 6 87 23 45 67	19 Rue de Lille	Paris	Île-de-France	75007	France	blank	22000	608	0	350	2000	declined	2023-04-12T15:43:00	low income	system_auto
A100008	student	0	USD	2023-04-12T12:12:00	Elijah	Johnson	2003-08-18	elijah.johnson@gmail.com	+1 212-555-0194	120 W 57th St	New York	NY	10019	USA	blank	0	631	0	0	800	declined	2023-04-13T13:55:00	no income, student	system_auto
A100009	student	0	TRY	2023-04-13T08:18:00	Sara	Yilmaz	2000-04-21	sara.yilmaz@mail.com.tr	blank	Ferah Sokak 10	Istanbul	Marmara	34000	Turkey	blank	2500	604	0	0	600	approved	2023-04-15T09:23:00	income from stipend	ekoc
A100010	employed	2	JPY	2023-04-13T18:45:00	Tomoko	Sato	1982-03-03	tomoko.sato@mail.jp	+81 80-1234-5678	2-7-3 Shibuya	Tokyo	Tokyo	150-0002	Japan	Rakuten	9200000	762	1	70000	350000	approved	2023-04-16T10:18:00	excellent financials	system_auto
A100011	retired	2	RUB	2023-04-14T08:33:00	Olga	Ivanova	1961-11-18	olga.ivanova@mail.ru	blank	Lenina 21	Moscow	Moscow	117312	Russia	blank	18500	538	1	900	1100	declined	2023-04-16T10:20:00	low credit score	auto_decision
A100012	self-employed	3	EUR	2023-04-14T13:27:00	Theo	Papadopoulos	1989-07-09	theo.papadopoulos@mail.gr	+30 691 234 5678	Ermou 4	Athens	Attica	10563	Greece	Papadopoulos Consulting	134000	810	1	3200	22000	approved	2023-04-16T11:20:00	excellent score	system_auto
A100013	employed	2	CNY	2023-04-15T12:11:00	Jin	Li	1987-05-24	jin.li@mail.cn	+86 138 1234 5678	23 Nanjing Road	Shanghai	Shanghai	200000	China	Alibaba Group	175000	699	0	920	16000	approved	2023-04-17T09:10:00	good credit profile	system_auto
A100014	employed	1	SEK	2023-04-16T09:29:00	Anna	Svensson	1994-01-16	anna.svensson@mail.se	+46 70 123 4567	Vasagatan 18	Stockholm	Stockholm	11120	Sweden	Volvo	586000	721	0	1800	23000	approved	2023-04-18T11:44:00	stable employment	hberg
A100015	unemployed	0	PKR	2023-04-16T14:44:00	Fatima	Khalid	1999-03-10	fatima.khalid@yahoo.com.pk	+92 300 1234567	34 Mall Road	Lahore	Punjab	54000	Pakistan	blank	0	412	0	0	200	declined	2023-04-18T09:55:00	no income	system_auto
A100016	employed	2	USD	2023-04-17T09:57:00	Matthew	Smith	1978-07-02	matthew.smith@mail.com	+1 310-555-0201	515 Ocean Ave	Santa Monica	CA	90402	USA	Tesla	112000	784	1	2200	18000	approved	2023-04-19T10:03:00	excellent profile	rsanchez
A100017	employed	2	AUD	2023-04-18T15:11:00	Olivia	Evans	1993-02-05	olivia.evans@mail.com.au	+61 412 345 678	33 Collins St	Melbourne	VIC	3000	Australia	ANZ Bank	73000	701	1	1150	9500	approved	2023-04-20T09:29:00	approved: employed, good score	mmartin
A100018	employed	2	QAR	2023-04-19T11:20:00	Ahmed	Hassan	1986-08-28	ahmed.hassan@mail.qa	+974 5551 2345	18 Al Sadd St	Doha	Doha	12211	Qatar	QNB	156000	719	1	2600	20000	approved	2023-04-21T10:15:00	reliable employment	system_auto
A100019	retired	1	RUB	2023-04-20T13:19:00	Viktor	Sokolov	1957-04-03	viktor.sokolov@mail.ru	blank	Nevsky Prospekt 14	Saint Petersburg	Leningrad	191186	Russia	blank	14200	468	1	400	900	declined	2023-04-22T08:38:00	low income, retired	auto_decision
A100020	employed	2	ZAR	2023-04-20T15:55:00	Samuel	Nkosi	1983-01-22	samuel.nkosi@mail.co.za	+27 82 123 4567	27 Vilakazi St	Johannesburg	Gauteng	1804	South Africa	Nedbank	550000	728	1	4800	15000	approved	2023-04-21T10:05:00	stable job, good score	lmahlangu

What the 200 rows show

from the 200-row sample

Self-employed (employment status) stands out: mean existing_credit_cards is 3.5, against 1.3 for the rest.

2median existing_credit_cards
2application statuses
46address countries
47reviewed_by values
59,850median annual_income
708.5median credit_score

Mean existing_credit_cards by employment_status200 rows

existing_credit_cards200 rows, in bands of 0.5

Median 2, from 0 to 5.

income_currency200 rows · top 10 of 38 values

EUR33
SEK14
USD13
RUB13
EGP9
INR8
GBP7
GHS7
CNY7
QAR7

25 columns by typefrom the column list below

string 16
integer 3
float 3
date 1
datetime 2

Columns

25 columns in three groups

blueprint · 25 columns
column	type	description	example
Text 16 columns
`application_id`	string	Unique identifier for each credit card applicationunique	A100001
`applicant_first_name`	string	Applicant's first name	Lucas
`applicant_last_name`	string	Applicant's last name	Weber
`applicant_email`	string	Applicant's email address	lucas.weber@mail.de
`applicant_phone`	string	Applicant's phone numberoptional	+49 1512 3487321
`address_street`	string	Applicant's street address	Leipziger Platz 8
`address_city`	string	Applicant's city	Berlin
`address_state`	string	Applicant's state or province	Berlin
`address_postal_code`	string	Applicant's postal or ZIP code	10117
`address_country`	string	Applicant's country of residence	Germany
`employment_status`	string	Applicant's current employment status6 values	employed
`employer_name`	string	Name of applicant's employer (if employed)optional	Siemens AG
`income_currency`	string	Currency code for annual income (ISO 4217 format)	EUR
`application_status`	string	Outcome of the credit card applicationapproved · declined	approved
`decision_reason`	string	Primary reason for approval or decline (e.g., 'low credit score', 'high income', 'existing debt')optional	good credit history
`reviewed_by`	string	Identifier of the staff or system making the decisionoptional	jmueller
Numbers 6 columns
`annual_income`	float	Applicant's reported annual income in local currency0 or more	79000
`credit_score`	integer	Applicant's credit score at time of application300 to 900 · optional	756
`existing_credit_cards`	integer	Number of existing credit cards held by the applicant0 or more · optional	2
`existing_loans`	integer	Number of existing loans held by the applicant0 or more · optional	0
`total_monthly_debt_payments`	float	Total monthly debt payments by the applicant0 or more · optional	450
`requested_credit_limit`	float	Credit limit requested by the applicant0 or more	12000
Dates and times 3 columns
`application_date`	datetime	Date and time the application was submitted	2023-04-03T10:15:00
`applicant_date_of_birth`	date	Applicant's date of birth	1991-06-22
`decision_date`	datetime	Date and time the application decision was made	2023-04-04T14:31:00

Use it for

A finance dashboard
Existing_credit_cards by employment_status and a breakdown of income_currency. Excel, Power BI or Tableau.
Why do the 17 self-employed rows have a mean existing_credit_cards of 3.5?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications200
A1000012employed
A1000021employed
A1000033self-emp…
A software demo
Believable applications with application_date, applicant_first_name and applicant_last_name to fill a screen in front of a buyer.

This dataset200 rows25 columns

Yours1,000 rows25 columnsaddress_street: UK only

blueprint · credit-card-application-approvals

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 25 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

Each record includes applicant demographics
Credit history and score provided
Approval outcome labeled
Track application channel (web, branch, phone)
Include offer type and credit limit

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: credit-card-application-approvals

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?