Finance
30 columns
200 rows
8 formats

Finance Loan Approval Modeling

This dataset contains detailed records of loan applications, including applicant demographics, financial profiles, loan request details, approval outcomes, and risk indicators. It is ideal for building predictive models for loan approval, credit risk assessment, and applicant profiling in banking and fintech environments.

last updated 20 Aug 2025
by GoMask

The brief that made it

Credit risk modeling and loan approval prediction

Sample rows

preview · 8 of 200 rows · all 30 columns

application_idstring	loan_purposestring	loan_term_monthsinteger	default_historyboolean	address_citystring	applicant_idstring	application_datedate	loan_amountfloat	applicant_incomefloat	applicant_employment_statusstring	applicant_employer_namestring	applicant_job_titlestring	applicant_ageinteger	applicant_genderstring	marital_statusstring	number_of_dependentsinteger	address_streetstring	address_statestring	address_postal_codestring	address_countrystring	credit_scoreinteger	existing_debtfloat	monthly_expensesfloat	collateral_typestring	collateral_valuefloat	co_applicant_idstring	loan_approval_statusstring	approval_datedate	approved_loan_amountfloat	interest_ratefloat
APP0001	home	72	false	Chicago	APL0001	2023-11-14	34000	85000	employed	BrightHouse Realty	Sales Manager	32	female	married	2	728 Willow St	IL	60616	USA	782	4500	2100	property	250000	blank	approved	2023-11-16	34000	3.95
APP0002	auto	36	false	Los Angeles	APL0002	2024-02-09	15000	61000	employed	Urban Motors	Technician	27	male	single	0	1119 Oakwood Ave	CA	90015	USA	679	1200	1800	vehicle	16000	APL0005	approved	2024-02-11	14000	5.85
APP0003	personal	24	false	San Jose	APL0003	2022-05-22	9000	38000	employed	RiverTech Solutions	IT Support	29	male	single	1	2451 Elm Dr	CA	95110	USA	648	2000	1400	none	0	blank	rejected	2022-05-25	blank	blank
APP0004	home	180	false	Miami	APL0004	2023-08-19	120000	120000	self-employed	Lakeside Consulting	Principal Consultant	43	female	married	3	9632 Maple Rd	FL	33127	USA	844	8000	3200	property	420000	blank	approved	2023-08-22	118000	2.95
APP0005	education	18	false	Ann Arbor	APL0005	2023-04-18	5500	14000	student	blank	blank	21	female	single	0	782 College Row	MI	48109	USA	519	0	900	none	0	blank	rejected	2023-04-19	blank	blank
APP0006	auto	60	false	Dallas	APL0006	2021-07-11	26000	56000	employed	Sunshine Electronics	Account Executive	35	male	married	2	6558 Pine St	TX	75201	USA	672	3200	2000	vehicle	27000	blank	approved	2021-07-13	25500	5.1
APP0007	personal	12	true	Madison	APL0007	2022-11-05	7200	35000	unemployed	blank	blank	24	other	single	0	4183 Cedar Pl	WI	53703	USA	395	0	700	none	0	APL0012	rejected	2022-11-08	blank	blank
APP0008	personal	48	false	Boston	APL0008	2024-05-01	21000	52000	employed	NextGen Marketing	Marketing Analyst	31	female	single	0	937 Sunrise Blvd	MA	02118	USA	765	1800	1650	none	0	blank	approved	2024-05-05	21000	4.3
APP0009	home	240	false	Orlando	APL0009	2023-02-15	250000	250000	self-employed	HarborView Holdings	CEO	57	male	married	2	6107 Lake Dr	FL	32806	USA	888	85000	6000	property	1200000	APL0020	approved	2023-02-17	245000	3.15
APP0010	auto	84	false	San Francisco	APL0010	2022-06-07	42000	68000	employed	Golden Gate Motors	Service Advisor	38	male	married	1	4710 Mission Rd	CA	94110	USA	704	9000	2300	vehicle	43000	blank	approved	2022-06-09	42000	4.5
APP0011	home	360	false	Houston	APL0011	2023-09-17	920000	295000	self-employed	Peak Realty Group	Managing Partner	49	male	married	3	4146 Bay St	TX	77002	USA	822	99000	7100	property	2200000	blank	approved	2023-09-19	900000	3.1
APP0012	personal	24	false	Boulder	APL0012	2023-03-13	7800	24000	student	blank	blank	23	prefer_not_to_say	single	0	1625 Spruce Dr	CO	80302	USA	488	0	950	none	0	blank	rejected	2023-03-15	blank	blank
APP0013	business	96	false	Austin	APL0013	2024-04-11	67000	112000	self-employed	GrowthPath Ventures	Founder	39	male	married	2	1836 Cedar Ave	TX	78701	USA	734	22000	3100	property	175000	blank	approved	2024-04-13	65000	4.75
APP0014	personal	12	true	Ann Arbor	APL0014	2021-10-03	5800	22000	unemployed	blank	blank	22	female	single	0	3111 Birch Ct	MI	48104	USA	362	0	750	none	0	blank	rejected	2021-10-06	blank	blank
APP0015	home	240	false	Minneapolis	APL0015	2021-03-28	180000	92000	employed	Cypress Construction	Project Lead	42	male	married	2	3076 Forest Dr	MN	55401	USA	710	43000	3500	property	480000	blank	approved	2021-03-30	175000	5.1
APP0016	auto	36	false	Houston	APL0016	2024-03-19	16000	42000	employed	AutoNation	Salesperson	28	male	single	0	5692 Cherry Ave	TX	77006	USA	698	2100	1250	vehicle	15500	blank	approved	2024-03-20	16000	6.25
APP0017	other	12	false	Madison	APL0017	2022-09-07	3200	18000	student	blank	blank	19	male	single	0	1524 Campus Way	WI	53715	USA	430	0	650	none	0	blank	pending	blank	blank	blank
APP0018	business	60	false	Austin	APL0018	2023-06-01	22000	73000	self-employed	StartUp Sphere	Owner	37	female	married	1	3200 Innovation Dr	TX	78705	USA	725	7400	2100	property	60000	blank	approved	2023-06-03	21500	5.55
APP0019	education	18	false	Boston	APL0019	2022-12-14	4250	12500	student	blank	blank	20	female	single	0	1900 College Dr	MA	02120	USA	467	0	720	none	0	blank	pending	blank	blank	blank
APP0020	home	300	false	Los Angeles	APL0020	2023-01-29	480000	175000	employed	Lincoln Insurance	Underwriting Director	55	male	married	2	5771 Lincoln Blvd	CA	90045	USA	853	62000	5800	property	2200000	blank	approved	2023-01-31	470000	2.98

What the 200 rows show

from the 200-row sample

Home (loan purpose) stands out: mean loan_term_months is 227.4, against 38.6 for the rest.

21%default_history = true
42median loan_term_months
3collateral types
3loan approval statuses
4applicant genders
4marital statuses

Mean loan_term_months by loan_purpose200 rows

loan_term_months200 rows, in bands of 50

Median 42, from 6 to 360.

address_city200 rows · top 10 of 29 values

Madison30
Ann Arbor17
Boston15
Houston14
Chicago13
Dallas12
Austin11
Boulder10
Denver10
Miami9

30 columns by typefrom the column list below

string 16
integer 4
float 7
date 2
boolean 1

Columns

30 columns in four groups

blueprint · 30 columns
column	type	description	example
Text 16 columns
`application_id`	string	Unique identifier for each loan applicationunique	APP0001
`applicant_id`	string	Unique identifier for the loan applicant	APL0001
`loan_purpose`	string	Purpose of the loan (e.g., home, auto, education, personal)6 values	home
`applicant_employment_status`	string	Current employment status of the applicantemployed · self-employed · unemployed · student · retired	employed
`applicant_employer_name`	string	Name of the applicant's employer (if employed or self-employed)optional	BrightHouse Realty
`applicant_job_title`	string	Job title of the applicant (if employed or self-employed)optional	Sales Manager
`applicant_gender`	string	Gender of the applicantmale · female · other · prefer_not_to_say · optional	female
`marital_status`	string	Marital status of the applicantsingle · married · divorced · widowed · other · optional	married
`address_street`	string	Street address of the applicant	728 Willow St
`address_city`	string	City of the applicant's address	Chicago
`address_state`	string	State or province of the applicant's address	IL
`address_postal_code`	string	Postal code of the applicant's address	60616
`address_country`	string	Country of the applicant's address	USA
`collateral_type`	string	Type of collateral provided for the loan (if any)property · vehicle · cash · none · other · optional	property
`co_applicant_id`	string	Unique identifier for co-applicant (if any)optional	APL0005
`loan_approval_status`	string	Final status of the loan applicationapproved · rejected · pending	approved
Numbers 11 columns
`loan_amount`	float	Requested loan amount in local currency0 or more	34000
`loan_term_months`	integer	Requested loan term in months1 or more	72
`applicant_income`	float	Applicant's annual income in local currency0 or more	85000
`applicant_age`	integer	Age of the applicant in years18 to 100	32
`number_of_dependents`	integer	Number of dependents supported by the applicant0 or more · optional	2
`credit_score`	integer	Applicant's credit score as reported by a credit bureau300 to 900	782
`existing_debt`	float	Total outstanding debt of the applicant in local currency0 or more · optional	4500
`monthly_expenses`	float	Applicant's average monthly expenses in local currency0 or more · optional	2100
`collateral_value`	float	Estimated value of the collateral in local currency0 or more · optional	250000
`approved_loan_amount`	float	Final approved loan amount (if different from requested)0 or more · optional	34000
`interest_rate`	float	Interest rate assigned to the approved loan0 to 100 · optional	3.95
Dates and times 2 columns
`application_date`	date	Date when the loan application was submitted	2023-11-14
`approval_date`	date	Date when the loan was approved or rejectedoptional	2023-11-16
True or false 1 column
`default_history`	boolean	Indicates if the applicant has any history of loan defaultoptional	false

Use it for

A finance dashboard
The default_history rate, loan_term_months by loan_purpose and a breakdown of address_city. Excel, Power BI or Tableau.
Why do the 42 home rows have a mean loan_term_months of 227.4?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications200
APP000172home
APP000712personal
APP001412personal
A software demo
Believable applications with applicant_id, application_date and loan_amount to fill a screen in front of a buyer.

This dataset200 rows30 columns

Yours1,000 rows30 columnsaddress_street: UK only

blueprint · finance-loan-approval-modeling

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 30 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

Include applicant demographics
Track credit score distributions
Segment approved vs denied applications
Flag high-risk profiles

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: finance-loan-approval-modeling

What should your data show?

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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?