Finance
29 columns
200 rows
8 formats

Credit Card Application Decisions

This dataset contains detailed synthetic records of credit card applications, including applicant demographics, financial profiles, application outcomes, and risk assessments. It is ideal for validating credit scoring models, detecting bias, and supporting regulatory compliance or fairness analysis in financial services. The flat schema design enables seamless integration with analytics and machine learning workflows.

last updated 21 Jul 2025
by GoMask

The brief that made it

Credit scoring model validation and benchmarking

Sample rows

preview · 8 of 200 rows · all 29 columns

application_idstring	employment_statusstring	ageinteger	is_fraud_suspectedboolean	reviewed_bystring	applicant_idstring	application_datedate	decision_datedate	application_statusstring	approval_reasonstring	credit_limit_requestedfloat	credit_limit_grantedfloat	annual_incomefloat	occupationstring	genderstring	marital_statusstring	number_of_dependentsinteger	residential_statusstring	years_at_current_addressfloat	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	credit_scoreinteger	existing_debtfloat	monthly_expensesfloat	risk_scorefloat	risk_categorystring
APP001	employed	32	false	officer_7	CUST1001	2021-03-21	2021-03-23	approved	Strong credit history, stable employment	12000	10000	95000	Software Engineer	female	single	0	rent	3.2	1452 W Market St	Columbus	OH	43215	USA	784	6500	2400	0.19	low
APP002	other	41	false	vip_officer	CUST1002	2022-08-11	2022-08-13	approved	High income, excellent credit	250000	250000	460000	Senior Executive	male	married	2	own	2.5	8011 Goldcrest Ave	Dallas	TX	75201	USA	881	20000	18000	0.04	low
APP003	unemployed	27	false	officer_2	CUST1003	2023-01-18	2023-01-20	declined	Low credit score, insufficient income	3500	0	17000	blank	female	single	1	rent	1.1	92 Parkside Dr	Rochester	NY	14623	USA	423	2300	900	0.79	high
APP004	employed	35	false	blank	CUST1004	2024-02-10	2024-02-10	pending	blank	10000	blank	73000	Project Manager	male	married	1	own	4.3	2216 Willow Lane	Phoenix	AZ	85016	USA	735	9000	2800	0.31	low
APP005	employed	29	false	system_auto	CUST1005	2020-12-02	2020-12-03	approved	Low risk, high score	14000	13500	104000	Financial Analyst	male	single	0	rent	2.7	5901 Ridge Road	Atlanta	GA	30303	USA	802	4300	2650	0.1	low
APP006	employed	34	false	officer_5	CUST1006	2022-05-30	2022-06-01	approved	Good payment history	9000	7600	67000	Teacher	female	married	1	own	4.8	3330 Elm St	Austin	TX	78752	USA	765	8500	2250	0.22	low
APP007	unemployed	39	false	officer_3	CUST1007	2022-10-14	2022-10-15	declined	High debt-to-income ratio	5000	0	34000	blank	male	divorced	2	other	0.7	147 Cedar Ave	Detroit	MI	48201	USA	350	22500	1700	0.95	high
APP008	self-employed	36	false	officer_8	CUST1008	2023-08-09	2023-08-10	approved	Stable income, good credit	15000	13500	91000	Freelance Designer	female	married	1	own	3.7	7423 Lakeview Dr	Seattle	WA	98115	USA	796	9800	2600	0.13	low
APP009	retired	68	false	system_auto	CUST1009	2020-04-27	2020-04-28	approved	Retired, low risk profile	6000	6000	37000	blank	male	widowed	0	own	23.4	2057 Oakridge Dr	Madison	WI	53704	USA	746	8000	1550	0.27	low
APP010	employed	30	false	officer_1	CUST1010	2022-09-04	2022-09-06	approved	Strong financials, low risk	11000	9500	87000	Nurse Practitioner	female	single	0	rent	3.4	1910 Winding Way	San Diego	CA	92103	USA	763	5200	2450	0.16	low
APP011	employed	33	false	officer_4	CUST1011	2021-02-14	2021-02-15	approved	Solid income, prior relationship	15000	12000	101000	Account Manager	male	married	1	own	1.9	2143 Kingston Ave	Chicago	IL	60616	USA	811	6700	2800	0.14	low
APP012	employed	34	false	officer_7	CUST1001	2023-04-05	2023-04-07	approved	Improved credit, increased income	16000	14500	112000	Software Engineer	female	single	0	own	5.1	1452 W Market St	Columbus	OH	43215	USA	819	4800	2650	0.11	low
APP013	student	22	false	blank	CUST1012	2023-10-28	2023-10-28	pending	blank	2750	blank	15000	Graduate Student	male	single	0	with parents	22	1281 College Blvd	Tempe	AZ	85281	USA	661	850	800	0.43	medium
APP014	self-employed	44	false	blank	CUST1013	2023-07-15	2023-07-15	pending	blank	9000	blank	53000	Independent Consultant	female	divorced	1	rent	2.9	3081 Forest View Rd	Des Moines	IA	50309	USA	693	11000	2100	0.34	medium
APP015	unemployed	24	false	officer_9	CUST1014	2021-08-19	2021-08-20	declined	High risk score, prior defaults	2000	0	19500	blank	non-binary	single	0	rent	0.8	1096 Edgewater Ct	Buffalo	NY	14202	USA	337	3100	950	0.98	high
APP016	other	29	true	officer_fraud	CUST1015	2020-06-11	2020-06-13	declined	High risk, suspected fraud	8700	0	29500	Gig Worker	prefer not to say	single	1	other	0.6	Unknown	Unknown	Unknown	00000	Unknown	315	12500	1400	0.99	high
APP017	employed	36	false	officer_4	CUST1011	2024-03-01	2024-03-03	approved	Repeat customer, excellent repayment	17000	17000	117000	Account Manager	male	married	2	own	3.1	2143 Kingston Ave	Chicago	IL	60616	USA	841	7800	3040	0.08	low
APP018	other	38	false	vip_officer	CUST1016	2021-12-10	2021-12-13	approved	Low risk, high income	250000	250000	1100000	Professional Athlete	male	married	3	own	3.7	9322 Bayside Rd	Los Angeles	CA	90024	USA	890	41000	22500	0.05	low
APP019	student	19	false	blank	CUST1017	2022-02-22	2022-02-22	pending	blank	3200	blank	12500	College Student	female	single	0	with parents	19	2005 Briarwood Rd	Boulder	CO	80303	USA	492	0	680	0.44	medium
APP020	unemployed	42	false	officer_2	CUST1018	2023-04-22	2023-04-23	declined	Recent bankruptcy, high risk	7800	0	22000	blank	female	separated	2	rent	0.9	5107 E Lake Dr	Cleveland	OH	44114	USA	317	17000	1800	0.97	high

What the 200 rows show

from the 200-row sample

Retired (employment status) stands out: mean age is 70.3, against 33.1 for the rest.

7%is_fraud_suspected = true
33.5median age
2address countries
3application statuses
3risk categories
4residential statuses

Mean age by employment_status200 rows

age200 rows, in bands of 10

Median 33.5, from 19 to 77.

reviewed_by170 rows with a value · 30 left blank

system_auto30
vip_officer22
officer_fraud13
officer_78
officer_86
officer_25
officer_35
officer_125
officer_14
officer_44

29 columns by typefrom the column list below

string 16
integer 3
float 7
date 2
boolean 1

Columns

29 columns in four groups

blueprint · 29 columns
column	type	description	example
Text 16 columns
`application_id`	string	Unique identifier for each credit card applicationunique	APP001
`applicant_id`	string	Unique identifier for the applicant (anonymized if necessary)	CUST1001
`application_status`	string	Final decision on the applicationapproved · declined · pending	approved
`approval_reason`	string	Reason for approval or decline (if available)optional	Low risk, high score
`employment_status`	string	Current employment status of the applicant6 values	employed
`occupation`	string	Applicant's occupation or job titleoptional	Software Engineer
`gender`	string	Applicant's self-reported gendermale · female · non-binary · prefer not to say · other · optional	female
`marital_status`	string	Applicant's marital status6 values · optional	single
`residential_status`	string	Type of residence (e.g., own, rent, with parents, other)own · rent · with parents · other · optional	rent
`address_street`	string	Applicant's street addressoptional	1452 W Market St
`address_city`	string	Applicant's city of residenceoptional	Columbus
`address_state`	string	Applicant's state or province of residenceoptional	OH
`address_postal_code`	string	Applicant's postal or ZIP codeoptional	43215
`address_country`	string	Applicant's country of residence2 countries · optional	USA
`risk_category`	string	Categorical risk assessment (e.g., low, medium, high)low · medium · high	low
`reviewed_by`	string	Identifier for the credit officer or automated system that reviewed the applicationoptional	officer_7
Numbers 10 columns
`credit_limit_requested`	float	Credit limit amount requested by the applicant0 or more	12000
`credit_limit_granted`	float	Credit limit amount granted (if approved)0 or more · optional	10000
`annual_income`	float	Applicant's reported annual income0 or more	95000
`age`	integer	Applicant's age in years18 or more	32
`number_of_dependents`	integer	Number of dependents the applicant has0 or more · optional	0
`years_at_current_address`	float	Number of years applicant has lived at their current address0 or more · optional	3.2
`credit_score`	integer	Applicant's credit score at the time of application300 to 900	784
`existing_debt`	float	Total outstanding debt reported by the applicant0 or more · optional	6500
`monthly_expenses`	float	Applicant's estimated monthly expenses0 or more · optional	2400
`risk_score`	float	Risk assessment score assigned during application review (e.g., probability of default)0 to 1	0.19
Dates and times 2 columns
`application_date`	date	Date when the credit card application was submitted	2021-03-21
`decision_date`	date	Date when the application decision was madeoptional	2021-03-23
True or false 1 column
`is_fraud_suspected`	boolean	Indicates if the application was flagged for suspected fraudoptional	false

Use it for

A finance dashboard
The is_fraud_suspected rate, age by employment_status and a breakdown of reviewed_by. Excel, Power BI or Tableau.
Why do the 17 retired rows have a mean age of 70.3?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications200
APP00132employed
APP01629other
APP03437other
A software demo
Believable applications with applicant_id, application_date and decision_date to fill a screen in front of a buyer.

This dataset200 rows29 columns

Yours1,000 rows29 columnsyears_at_current_address: UK only

blueprint · credit-card-application-decisions

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 29 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

Applications include income, debt, and employment status
Approval logic based on creditworthiness and policy
Rejected cases include reason codes
Demographics anonymized for fairness testing
Label risky and borderline 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-card-application-decisions

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?