Finance
30 columns
200 rows
8 formats

Loan Application Approval Analytics

This dataset provides detailed records of loan applications, including applicant demographics, financial profiles, loan characteristics, approval outcomes, and supporting information such as co-applicant and collateral details. It enables financial institutions to analyze approval trends, assess risk factors, and refine lending criteria for improved decision-making and portfolio management.

last updated 20 Aug 2025
by GoMask

The brief that made it

Credit risk modeling and applicant segmentation

Sample rows

preview · 8 of 200 rows · all 30 columns

application_idstring	loan_typestring	loan_term_monthsinteger	applicant_employment_statusstring	applicant_idstring	applicant_namestring	applicant_dobdate	applicant_genderstring	applicant_marital_statusstring	applicant_annual_incomefloat	applicant_credit_scoreinteger	applicant_address_streetstring	applicant_address_citystring	applicant_address_statestring	applicant_address_postal_codestring	applicant_address_countrystring	loan_amountfloat	loan_interest_ratefloat	application_datedate	approval_statusstring	approval_datedate	coapplicant_idstring	coapplicant_namestring	coapplicant_annual_incomefloat	coapplicant_credit_scoreinteger	property_valuefloat	collateral_typestring	existing_debtfloat	debt_to_income_ratiofloat	approval_reasonstring
APP-00001	mortgage	240	employed	USER-1001	Sophia Martinez	1990-04-12	female	married	85000	720	872 Oakwood Drive	Dallas	TX	75201	USA	340000	4.2	2022-06-10	approved	2022-06-12	USER-1002	Carlos Martinez	61000	705	495000	real_estate	25000	21.7	Strong income and credit profile; DTI acceptable.
APP-00002	business	96	self-employed	USER-1003	Jonas Becker	1987-09-30	male	single	123000	760	14 Hauptstrasse	Berlin	BE	10115	Germany	210000	6.1	2021-11-05	approved	2021-11-08	blank	blank	blank	blank	315000	real_estate	12000	17.5	Business plan reviewed; high income and credit.
APP-00003	student	48	student	USER-1004	Fatima Ahmed	1999-02-21	female	single	1200	420	112 Crescent Road	Cairo	C	11956	Egypt	7000	7.9	2023-03-18	rejected	2023-03-20	blank	blank	blank	blank	0	none	0	0	Insufficient income and credit; no coapplicant.
APP-00004	auto	60	employed	USER-1005	Emily Chen	1993-06-15	female	single	47000	655	88 Orchard Lane	Toronto	ON	M4B1B3	Canada	22000	5.5	2024-01-13	approved	2024-01-15	blank	blank	blank	blank	33000	vehicle	4000	12.8	Credit and income adequate for auto loan.
APP-00005	personal	36	retired	USER-1006	George Okeke	1963-11-02	male	married	32000	630	57 Unity Street	Lagos	LA	101223	Nigeria	9500	9.8	2023-09-27	rejected	2023-09-29	blank	blank	blank	blank	0	none	21000	66.5	Debt-to-income ratio too high for approval.
APP-00006	personal	48	employed	USER-1007	Priya Sharma	1997-10-07	female	single	41000	670	29 Lotus Avenue	Mumbai	MH	400021	India	12000	11.2	2022-08-04	approved	2022-08-06	blank	blank	blank	blank	0	none	3000	7.3	Stable employment, moderate DTI.
APP-00007	business	120	self-employed	USER-1008	Mohammed Al-Farsi	1982-03-10	male	married	153000	805	101 Jasmine Street	Dubai	DU	00051	UAE	390000	5.3	2024-02-27	approved	2024-03-01	USER-1009	Layla Al-Farsi	87000	795	580000	real_estate	17000	13.1	Excellent business income and credit.
APP-00008	auto	36	employed	USER-1010	Chloe Dubois	1995-12-03	female	single	56000	710	47 Rue de la Paix	Paris	IDF	75002	France	18000	4.8	2021-08-11	approved	2021-08-13	blank	blank	blank	blank	24000	vehicle	2000	3.6	Good income and strong credit for auto loan.
APP-00009	personal	60	employed	USER-1011	Samuel Lee	1976-07-22	male	divorced	67000	690	212 Willow Road	San Francisco	CA	94110	USA	18000	10.6	2022-10-02	approved	2022-10-05	blank	blank	blank	blank	0	none	8000	11.9	Income and credit qualify for personal loan.
APP-00010	personal	24	retired	USER-1012	Olga Petrova	1969-02-17	female	widowed	23000	610	19 Pushkin Street	Moscow	MOW	103012	Russia	4000	13.4	2023-07-21	rejected	2023-07-23	blank	blank	blank	blank	0	none	12000	52.2	Low income and high debt-to-income ratio.
APP-00011	auto	72	employed	USER-1013	Liam Johnson	1992-05-04	male	single	56000	700	303 Maple Boulevard	Chicago	IL	60610	USA	27000	4.6	2023-05-17	approved	2023-05-19	blank	blank	blank	blank	39000	vehicle	5000	8.9	Good income and credit; DTI low.
APP-00012	mortgage	180	employed	USER-1014	Yasmin Saleh	1984-01-11	female	married	108000	765	381 Palm Grove	Amman	AM	11181	Jordan	290000	5.7	2022-03-23	approved	2022-03-27	USER-1015	Omar Saleh	95000	755	445000	real_estate	8000	10.5	Income and credit highly suitable for mortgage.
APP-00013	business	84	self-employed	USER-1016	Aiden Murphy	1988-09-28	male	married	99000	720	29 Abbey Road	Dublin	D	D02WY88	Ireland	120000	7	2022-09-10	approved	2022-09-13	blank	blank	blank	blank	155000	real_estate	9000	6.8	Business plan feasible; credit meets threshold.
APP-00014	personal	36	employed	USER-1017	Lucia Rossi	1991-05-26	female	single	49000	680	12 Via Milano	Rome	RM	00184	Italy	13000	9.7	2023-12-03	approved	2023-12-05	blank	blank	blank	blank	0	none	1000	2	Meets all requirements for personal loan.
APP-00015	personal	18	retired	USER-1018	Markus Lindberg	1958-11-13	male	widowed	18000	595	6 Skogsvägen	Stockholm	ST	11421	Sweden	2500	12.8	2024-03-02	rejected	2024-03-05	blank	blank	blank	blank	0	none	2000	11.1	Income below minimum threshold.
APP-00016	auto	96	employed	USER-1019	Emma Brown	1994-07-18	female	single	66000	755	89 Vine Street	London	LDN	EC1A9LP	UK	38000	3.9	2022-12-11	approved	2022-12-13	blank	blank	blank	blank	49000	vehicle	7000	10.6	High credit score; income sufficient.
APP-00017	student	24	student	USER-1020	Jacob Kim	2000-08-03	male	single	0	340	77 Han River Dr	Seoul	SEO	04522	South Korea	5000	8.3	2023-05-06	pending	blank	blank	blank	blank	blank	0	none	0	0	blank
APP-00018	mortgage	180	employed	USER-1021	Linda Muller	1989-02-02	female	married	96000	735	31 Lindenweg	Zurich	ZH	8004	Switzerland	270000	3.7	2021-06-07	approved	2021-06-10	blank	blank	blank	blank	395000	real_estate	12000	8.9	Strong credit and income for mortgage.
APP-00019	personal	48	employed	USER-1022	James Smith	1971-01-25	male	married	53000	682	100 Oak Street	Sydney	NSW	2000	Australia	17000	8.7	2022-07-20	approved	2022-07-23	blank	blank	blank	blank	0	none	6000	10.1	Meets requirements for personal loan.
APP-00020	auto	60	employed	USER-1023	Marta Fernandez	1985-03-11	female	divorced	58000	670	115 Calle Mayor	Madrid	MD	28013	Spain	21000	6.1	2023-08-09	approved	2023-08-11	blank	blank	blank	blank	26500	vehicle	3500	6	Satisfactory income and credit.

What the 200 rows show

from the 200-row sample

Mortgage (loan type) stands out: mean loan_term_months is 233.0, against 49.7 for the rest.

48median loan_term_months
2applicant genders
3approval statuses
3collateral types
4applicant marital statuses
48applicant address countries

Mean loan_term_months by loan_type200 rows

loan_term_months200 rows, in bands of 50

Median 48, from 12 to 360.

applicant_employment_status200 rows · 5 values

employed115
self-employed30
retired27
student21
unemployed7

30 columns by typefrom the column list below

string 17
integer 3
float 7
date 3

Columns

30 columns in three groups

blueprint · 30 columns
column	type	description	example
Text 17 columns
`application_id`	string	Unique identifier for each loan applicationunique	APP-00001
`applicant_id`	string	Unique identifier for each applicant	USER-1001
`applicant_name`	string	Full name of the applicant	Sophia Martinez
`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	married
`applicant_employment_status`	string	Employment status of the applicant6 values	employed
`applicant_address_street`	string	Street address of the applicant	872 Oakwood Drive
`applicant_address_city`	string	City of the applicant's address	Dallas
`applicant_address_state`	string	State or province of the applicant's address	TX
`applicant_address_postal_code`	string	Postal code of the applicant's address	75201
`applicant_address_country`	string	Country of the applicant's address	USA
`loan_type`	string	Type of loan applied for6 values	mortgage
`approval_status`	string	Outcome of the loan applicationapproved · rejected · pending · withdrawn	approved
`approval_reason`	string	Reason for approval or rejection (if applicable)optional	Good employment and DTI.
`coapplicant_id`	string	Unique identifier for co-applicant (if any)optional	USER-1002
`coapplicant_name`	string	Full name of the co-applicant (if any)optional	Carlos Martinez
`collateral_type`	string	Type of collateral provided (if any)real_estate · vehicle · cash · none · other · optional	real_estate
Numbers 10 columns
`applicant_annual_income`	float	Annual income of the applicant in local currency0 or more	85000
`applicant_credit_score`	integer	Credit score of the applicant300 to 850	720
`loan_amount`	float	Amount of loan requested0 or more	340000
`loan_term_months`	integer	Loan term in months1 or more	240
`loan_interest_rate`	float	Annual interest rate for the loan (percentage)0 to 100	4.2
`coapplicant_annual_income`	float	Annual income of the co-applicant (if any)0 or more · optional	61000
`coapplicant_credit_score`	integer	Credit score of the co-applicant (if any)300 to 850 · optional	705
`property_value`	float	Estimated value of the property (for secured loans)0 or more · optional	495000
`existing_debt`	float	Total existing debt obligations of the applicant0 or more · optional	25000
`debt_to_income_ratio`	float	Debt-to-income ratio of the applicant (percentage)0 to 100 · optional	21.7
Dates and times 3 columns
`applicant_dob`	date	Date of birth of the applicant	1990-04-12
`application_date`	date	Date when the loan application was submitted	2022-06-10
`approval_date`	date	Date when the loan application was approved or rejectedoptional	2022-06-12

Use it for

A finance dashboard
Loan_term_months by loan_type and a breakdown of applicant_employment_status. Excel, Power BI or Tableau.
Why do the 29 mortgage rows have a mean loan_term_months of 233.0?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications200
APP-00001240mortgage
APP-0000296business
APP-0000348student
A software demo
Believable applications with applicant_id, applicant_name and applicant_dob to fill a screen in front of a buyer.

This dataset200 rows30 columns

Yours1,000 rows30 columnsapplicant_address_street: UK only

blueprint · loan-application-approval-analytics

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 financial status
Track approval and rejection reasons
Exclude duplicate applications
Label manual and automated decisions

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-application-approval-analytics

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?