Finance
28 columns
200 rows
8 formats

Bank Loan Application Approvals

This dataset contains detailed synthetic records of bank loan applications, including applicant demographics, financial background, loan request details, and final approval or denial outcomes. It is ideal for developing and benchmarking predictive models for credit risk assessment, as well as for analyzing approval patterns and fairness in lending decisions.

opened 9 times
last updated 12 Jul 2025
by GoMask

The brief that made it

Developing and validating machine learning models for loan approval prediction

Sample rows

preview · 8 of 200 rows · all 28 columns

application_idstring	application_statusstring	applicant_credit_scoreinteger	denial_reasonstring	application_datedate	applicant_idstring	applicant_first_namestring	applicant_last_namestring	applicant_dobdate	applicant_genderstring	applicant_marital_statusstring	applicant_employment_statusstring	applicant_annual_incomefloat	applicant_existing_debtfloat	applicant_dependentsinteger	residence_street_addressstring	residence_citystring	residence_statestring	residence_postal_codestring	residence_countrystring	loan_amountfloat	loan_term_monthsinteger	loan_purposestring	collateral_typestring	collateral_valuefloat	decision_datedate	approved_amountfloat	interest_ratefloat
APP000001	approved	745	blank	2023-04-12	CUST1001	Liam	Nguyen	1991-07-21	male	single	employed	86000	2500	1	102 Main Avenue	New York	NY	10016	USA	250000	360	home_purchase	real_estate	350000	2023-04-14	240000	4.2
APP000002	approved	782	blank	2023-05-08	CUST1002	Priya	Sharma	1987-10-02	female	married	employed	112000	17500	2	431 MG Road	Mumbai	MH	400001	India	95000	84	home_improvement	none	0	2023-05-10	90000	6.3
APP000003	approved	676	blank	2023-03-15	CUST1003	John	Smith	1960-12-11	male	married	retired	32000	7000	0	44 Maple Drive	Toronto	ON	M5G2C3	Canada	18000	36	medical	none	0	2023-03-16	18000	6.9
APP000004	denied	380	Low income and credit score	2023-02-17	CUST1004	Fatima	Al-Farsi	2001-03-09	female	single	student	0	0	0	112 University Rd	Muscat	MA	113	Oman	6500	24	education	none	0	2023-02-18	0	0
APP000005	approved	710	blank	2023-01-22	CUST1005	Alejandro	Pereira	1979-08-15	male	married	self-employed	260000	12000	3	590 Avenida Paulista	Sao Paulo	SP	01310-000	Brazil	85000	60	business	cash	95000	2023-01-25	82000	9.5
APP000006	approved	795	blank	2023-05-23	CUST1006	Chen	Wang	1975-06-01	male	married	employed	142000	32000	2	80 Nanjing Rd	Shanghai	SH	200001	China	210000	120	home_purchase	real_estate	350000	2023-05-25	210000	3.8
APP000007	approved	670	blank	2023-04-04	CUST1007	Lea	Dubois	1995-11-02	female	single	employed	49000	2500	0	19 Rue Lafayette	Paris	IDF	75009	France	14000	24	vehicle	vehicle	11500	2023-04-07	13500	5.9
APP000008	denied	355	Insufficient income	2023-03-14	CUST1008	Grace	Kim	1999-01-18	female	single	student	0	500	0	24 Yonsei-ro	Seoul	11	03722	South Korea	12000	36	education	none	0	2023-03-16	0	0
APP000009	approved	735	blank	2023-01-11	CUST1009	James	Baker	1984-04-28	male	married	employed	107000	35000	2	553 Oak Lane	Chicago	IL	60615	USA	255000	360	home_purchase	real_estate	400000	2023-01-13	250000	4.5
APP000010	approved	670	blank	2023-03-20	CUST1010	Isabella	Garcia	1970-07-10	female	divorced	employed	54000	1800	1	299 Calle Mayor	Madrid	MD	28013	Spain	11000	36	vehicle	vehicle	9500	2023-03-21	10000	6.7
APP000011	approved	692	blank	2023-06-01	CUST1011	Olga	Kuznetsova	1988-09-26	female	single	self-employed	72000	9800	0	12 Nevsky Prospekt	St. Petersburg	SPE	191025	Russia	32000	60	business	none	0	2023-06-04	30000	8.2
APP000012	approved	708	blank	2023-02-27	CUST1012	Sven	Larsson	1957-11-03	male	widowed	retired	21000	1500	0	65 Vasagatan	Stockholm	AB	11120	Sweden	17000	24	medical	none	0	2023-03-01	16000	7.1
APP000013	denied	322	Low credit score	2023-05-01	CUST1013	Ahmed	El-Sayed	1998-05-06	male	single	student	0	0	0	21 Tahrir St	Cairo	C	11511	Egypt	8500	18	education	none	0	2023-05-04	0	0
APP000014	approved	755	blank	2023-03-28	CUST1014	Emily	Miller	1989-09-12	female	married	employed	92000	18000	2	888 Spruce St	San Francisco	CA	94117	USA	52000	84	home_improvement	none	0	2023-03-30	50000	5.5
APP000015	approved	662	blank	2023-01-30	CUST1015	Victor	Moreno	1992-02-22	male	single	employed	54000	6500	1	17 Calle 61	Merida	YU	97000	Mexico	17000	36	vehicle	vehicle	12000	2023-02-02	15000	7.4
APP000016	approved	804	blank	2023-04-23	CUST1016	Sophia	Chan	1985-06-19	female	single	self-employed	162000	24000	0	5 Nathan Road	Hong Kong	HK	999077	Hong Kong	70000	60	business	cash	70000	2023-04-26	68000	9
APP000017	approved	710	blank	2023-04-15	CUST1017	Samuel	Johnson	1965-01-30	male	widowed	retired	26000	2000	0	201 King St	Sydney	NSW	2000	Australia	12000	18	medical	none	0	2023-04-17	12000	7.3
APP000018	approved	695	blank	2023-02-09	CUST1018	Lucas	Martinez	1994-09-15	male	single	employed	59000	8800	0	999 Libertad	Buenos Aires	CBA	1000	Argentina	15000	24	vehicle	vehicle	10500	2023-02-12	14000	7.1
APP000019	approved	710	blank	2023-05-18	CUST1019	Amira	Mahmoud	1980-04-16	female	married	employed	99000	12000	2	50 Ramses St	Cairo	C	11511	Egypt	83000	72	debt_consolidation	none	0	2023-05-20	80000	6.2
APP000020	approved	660	blank	2023-06-14	CUST1020	Anna	Johansson	1955-02-14	female	widowed	retired	16500	500	0	77 Norra Hamngatan	Gothenburg	O	41106	Sweden	12000	18	medical	none	0	2023-06-15	12000	6.2

What the 200 rows show

from the 200-row sample

Approved (application status) stands out: mean applicant_credit_score is 712.1, against 337.6 for the rest.

690.5median applicant_credit_score
3applicant genders
5applicant marital statuses
5applicant employment statuses
5collateral types
8loan purposes

Mean applicant_credit_score by application_status200 rows

applicant_credit_score200 rows, in bands of 100

Median 690.5, from 308 to 804.

denial_reason40 rows with a value · 160 left blank

Low credit score9
Low income and credit6
Insufficient income5
Low income5
No income5
Low income and credit score2
No income source2
Unemployed applicant1
Student with no income1
Unemployed and low credit1

28 columns by typefrom the column list below

string 16
integer 3
float 6
date 3

Columns

28 columns in three groups

blueprint · 28 columns
column	type	description	example
Text 16 columns
`application_id`	string	Unique identifier for each loan applicationunique	APP000001
`applicant_id`	string	Unique identifier for the applicant	CUST1001
`applicant_first_name`	string	First name of the applicant	Liam
`applicant_last_name`	string	Last name of the applicant	Nguyen
`applicant_gender`	string	Gender of the applicantmale · female · other · prefer_not_to_say · optional	male
`applicant_marital_status`	string	Marital status of the applicant6 values · optional	single
`applicant_employment_status`	string	Current employment status of the applicant6 values	employed
`residence_street_address`	string	Street address of the applicant's residenceoptional	102 Main Avenue
`residence_city`	string	City of the applicant's residenceoptional	New York
`residence_state`	string	State or province of the applicant's residenceoptional	NY
`residence_postal_code`	string	Postal code of the applicant's residenceoptional	10016
`residence_country`	string	Country of the applicant's residenceoptional	USA
`loan_purpose`	string	Purpose of the loan as stated by the applicant8 values	home_purchase
`collateral_type`	string	Type of collateral provided, if any6 values · optional	real_estate
`application_status`	string	Final decision on the loan applicationapproved · denied	approved
`denial_reason`	string	Reason for denial if the application was not approvedoptional	Insufficient income
Numbers 9 columns
`applicant_annual_income`	float	Annual income of the applicant in local currency0 or more	86000
`applicant_credit_score`	integer	Credit score of the applicant at the time of application300 to 850	745
`applicant_existing_debt`	float	Total existing debt obligations of the applicant in local currency0 or more · optional	2500
`applicant_dependents`	integer	Number of dependents the applicant supports0 or more · optional	1
`loan_amount`	float	Requested loan amount in local currency0 or more	250000
`loan_term_months`	integer	Requested loan term in months1 or more	360
`collateral_value`	float	Estimated value of the collateral in local currency0 or more · optional	350000
`approved_amount`	float	Amount approved by the bank (0 if denied)0 or more	240000
`interest_rate`	float	Interest rate assigned to the loan (0 if denied)0 to 100	4.2
Dates and times 3 columns
`application_date`	date	Date when the loan application was submitted	2023-04-12
`applicant_dob`	date	Date of birth of the applicant	1991-07-21
`decision_date`	date	Date when the application decision was made	2023-04-14

Use it for

A finance dashboard
Applicant_credit_score by application_status and a breakdown of denial_reason. Excel, Power BI or Tableau.
Why do the 160 approved rows have a mean applicant_credit_score of 712.1?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications200
APP000001745approved
APP000002782approved
APP000003676approved
A software demo
Believable applications with application_date, applicant_id and applicant_first_name to fill a screen in front of a buyer.

This dataset200 rows28 columns

Yours1,000 rows28 columnsresidence_street_address: UK only

blueprint · bank-loan-application-approvals

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 28 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

Applicant demographics, income, and credit score
Loan amount and term per entry
Label as approved or denied
No real applicants

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: bank-loan-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?