Finance
28 columns
200 rows
8 formats

Loan Approval Decision Dataset

This dataset provides a comprehensive record of historical loan applications, including applicant demographics, financial profiles, loan details, and approval decisions. It is ideal for developing and validating credit scoring models, automating loan approval workflows, and analyzing lending risk factors across diverse applicant segments.

last updated 20 Aug 2025
by GoMask

The brief that made it

Credit scoring model development and validation

Sample rows

preview · 8 of 200 rows · all 28 columns

application_idstring	loan_typestring	interest_ratefloat	co_applicant_presentboolean	applicant_address_countrystring	applicant_idstring	application_datedate	approval_statusstring	loan_amountfloat	loan_term_monthsinteger	applicant_namestring	applicant_birth_datedate	applicant_genderstring	applicant_marital_statusstring	applicant_employment_statusstring	applicant_annual_incomefloat	applicant_credit_scoreinteger	applicant_address_streetstring	applicant_address_citystring	applicant_address_statestring	applicant_address_postal_codestring	co_applicant_annual_incomefloat	co_applicant_credit_scoreinteger	existing_debtfloat	number_of_dependentsinteger	property_ownershipstring	decision_datedate	decision_reasonstring
APP00001	personal	7.2	false	USA	USR001	2021-03-01	approved	18500.5	12	Jessica Lee	1992-06-10	female	single	employed	48000	720	1549 Liberty Ave	Philadelphia	PA	19102	blank	blank	1200	0	rented	2021-03-03	credit_ok
APP00002	student	4.1	true	South Korea	USR002	2023-07-11	pending	3200	36	Minho Park	2002-10-02	male	single	student	0	485	20 Gwanak-ro	Seoul	KR	08788	52000	730	0	0	rented	2023-07-13	awaiting_docs
APP00003	mortgage	2.9	false	Spain	USR003	2022-11-19	approved	185000	360	Ricardo Alvarez	1973-03-18	male	married	self-employed	92000	799	Calle Mayor 45	Madrid	MD	28013	blank	blank	15000	2	owned	2022-11-24	income_verified
APP00004	other	29.99	false	India	USR004	2020-06-15	rejected	650	6	Ravi Kumar	1996-08-20	male	single	unemployed	0	120	9 MG Road	Bengaluru	KA	560001	blank	blank	0	0	rented	2020-06-16	low_income
APP00005	auto	4.6	false	Russia	USR005	2023-01-22	approved	21800	60	Natalia Ivanova	1988-11-05	female	married	employed	66000	770	Prospekt Mira 33	Moscow	RU	129110	blank	blank	8400	1	owned	2023-01-26	credit_ok
APP00006	business	13.5	false	India	USR006	2022-03-30	approved	98500	48	Priya Sharma	1981-12-13	female	married	self-employed	118000	835	12 Nehru Place	Delhi	DL	110019	blank	blank	24000	2	owned	2022-04-02	business_docs_verified
APP00007	personal	10.2	true	USA	USR007	2021-09-04	pending	8000	24	David Miller	1995-03-15	male	single	employed	43000	670	2368 Cedar Blvd	San Jose	CA	95125	34000	650	3200	0	rented	2021-09-09	awaiting_docs
APP00008	mortgage	3.2	false	USA	USR008	2022-07-18	approved	420000	480	Annabelle Jones	1958-08-22	female	widowed	retired	34000	820	3702 Oakridge Dr	Dallas	TX	75220	blank	blank	0	0	owned	2022-07-21	retirement_income_ok
APP00009	auto	4.9	false	Germany	USR009	2020-02-11	approved	23000	36	Samuel Becker	1983-04-09	male	married	employed	68000	730	14 Lindenstrasse	Berlin	BE	10115	blank	blank	6000	1	owned	2020-02-15	credit_ok
APP00010	student	3	true	Canada	USR010	2021-01-07	pending	1500	24	Emily Brown	2003-05-22	female	single	student	0	410	75 College St	Toronto	ON	M5G 2A1	62000	810	0	0	rented	2021-01-10	awaiting_docs
APP00011	business	12.2	false	UAE	USR011	2022-05-05	approved	86000	36	Ahmed Mansoor	1979-02-14	male	married	self-employed	99000	798	32 Sheikh Zayed Rd	Dubai	DU	00000	blank	blank	8600	3	owned	2022-05-07	business_docs_verified
APP00012	personal	19.5	false	USA	USR012	2023-08-17	pending	980	6	Connor Murphy	1998-04-25	male	single	unemployed	0	250	821 Maple Lane	Boston	MA	02124	blank	blank	0	0	rented	2023-08-19	awaiting_docs
APP00013	personal	8	false	France	USR013	2021-02-21	approved	14500	24	Chloe Martin	1989-03-09	female	single	employed	57000	765	41 Rue du Renard	Paris	FR	75004	blank	blank	2200	0	rented	2021-02-23	credit_ok
APP00014	mortgage	3.5	false	UK	USR014	2020-09-12	approved	139500	240	Linda Carter	1965-12-05	female	divorced	retired	25000	783	19 Meadow Road	Manchester	ENG	M13 9PL	blank	blank	0	0	owned	2020-09-17	retirement_income_ok
APP00015	other	49.99	false	Romania	USR015	2022-10-16	rejected	1099.99	1	Marius Popescu	1997-10-26	male	single	unemployed	0	0	Str. Principala 12	Cluj-Napoca	CJ	400001	blank	blank	0	1	rented	2022-10-16	low_income_and_credit
APP00016	auto	5.3	false	USA	USR016	2021-04-29	approved	59000	72	Timothy White	1985-01-07	male	married	employed	84000	785	3667 Pinecrest Dr	Houston	TX	77059	blank	blank	12300	2	owned	2021-05-03	credit_ok
APP00017	business	15.7	true	India	USR017	2023-02-13	pending	34700	24	Rajesh Patel	1978-09-23	male	married	self-employed	98000	710	41 Ambedkar Marg	Ahmedabad	GJ	380001	33000	590	16000	3	owned	2023-02-17	awaiting_docs
APP00018	mortgage	2.2	false	USA	USR018	2021-06-22	approved	675000	360	William Turner	1953-03-01	male	widowed	retired	29500	897	8146 Timberwood Ln	Chicago	IL	60645	blank	blank	0	0	owned	2021-06-25	retirement_income_ok
APP00019	student	6.8	true	Spain	USR019	2022-01-23	pending	700	6	Sofia Martinez	2000-12-05	female	single	student	0	270	Avenida del Sol 19	Barcelona	CAT	08003	18000	620	0	0	rented	2022-01-26	awaiting_docs
APP00020	personal	39.99	false	Morocco	USR020	2020-05-17	rejected	1100	1	Fatima El Amrani	1992-02-27	female	single	unemployed	0	95	Rue Mohammed V 74	Casablanca	CAS	20000	blank	blank	0	0	rented	2020-05-17	low_credit_score

What the 200 rows show

from the 200-row sample

Other (loan type) stands out: mean interest_rate is 71.4, against 9.9 for the rest.

23%co_applicant_present = true
6.8median interest_rate
2applicant genders
3approval statuses
4applicant marital statuses
4property ownerships

Mean interest_rate by loan_type200 rows

interest_rate200 rows, in bands of 10

Median 6.8, from 2.1 to 100.0.

applicant_address_country200 rows · top 10 of 40 values

USA30
India16
Russia13
Germany12
Canada10
France9
UK9
Spain8
Ireland8
Morocco7

28 columns by typefrom the column list below

string 15
integer 4
float 5
date 3
boolean 1

Columns

28 columns in four groups

blueprint · 28 columns
column	type	description	example
Text 15 columns
`application_id`	string	Unique identifier for each loan applicationunique	APP00001
`applicant_id`	string	Unique identifier for the applicant	USR001
`approval_status`	string	Final decision on the loan applicationapproved · rejected · pending	approved
`loan_type`	string	Type of loan applied for6 values	personal
`applicant_name`	string	Full name of the applicant	Jessica Lee
`applicant_gender`	string	Gender of the applicantmale · female · other · prefer_not_to_say · optional	female
`applicant_marital_status`	string	Marital status of the applicantsingle · married · divorced · widowed · other · optional	single
`applicant_employment_status`	string	Employment status of the applicant6 values	employed
`applicant_address_street`	string	Street address of the applicant	1549 Liberty Ave
`applicant_address_city`	string	City of the applicant's address	Philadelphia
`applicant_address_state`	string	State or province of the applicant's address	PA
`applicant_address_postal_code`	string	Postal code of the applicant's address	19102
`applicant_address_country`	string	Country of the applicant's address	USA
`property_ownership`	string	Type of property ownership of the applicantowned · mortgaged · rented · other · optional	rented
`decision_reason`	string	Short description or code for the approval/rejection reasonoptional	credit_ok
Numbers 9 columns
`loan_amount`	float	Requested loan amount in local currency0 or more	18500.5
`loan_term_months`	integer	Duration of the loan in months1 or more	12
`interest_rate`	float	Annual interest rate (%) offered for the loan0 or more	7.2
`applicant_annual_income`	float	Annual income of the applicant in local currency0 or more	48000
`applicant_credit_score`	integer	Credit score of the applicant at the time of application0 to 999	720
`co_applicant_annual_income`	float	Annual income of the co-applicant, if present0 or more · optional	52000
`co_applicant_credit_score`	integer	Credit score of the co-applicant, if present0 to 999 · optional	730
`existing_debt`	float	Total outstanding debt of the applicant at the time of application0 or more	1200
`number_of_dependents`	integer	Number of dependents claimed by the applicant0 or more · optional	0
Dates and times 3 columns
`application_date`	date	Date when the loan application was submitted	2021-03-01
`applicant_birth_date`	date	Birth date of the applicant	1992-06-10
`decision_date`	date	Date when the loan approval decision was made	2021-03-03
True or false 1 column
`co_applicant_present`	boolean	Indicates if a co-applicant is present on the application	false

Use it for

A finance dashboard
The co_applicant_present rate, interest_rate by loan_type and a breakdown of applicant_address_country. Excel, Power BI or Tableau.
Why do the 21 other rows have a mean interest_rate of 71.4?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications200
APP000017.2personal
APP000024.1student
APP0000710.2personal
A software demo
Believable applications with applicant_id, application_date and approval_status to fill a screen in front of a buyer.

This dataset200 rows28 columns

Yours1,000 rows28 columnsapplicant_address_street: UK only

blueprint · loan-approval-decision-dataset

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

Application details with applicant financials
Approval/rejection flag based on criteria
Loan type and amount annotated
Supports binary and multi-class models

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: loan-approval-decision-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?