Finance
30 columns
200 rows
8 formats

Automated Loan Application Assessment

This dataset contains detailed synthetic records of loan applications, including applicant demographics, financial profiles, loan details, automated risk assessments, and approval or denial outcomes. It is ideal for developing and evaluating AI models for credit decisioning, risk analysis, and fair lending compliance, with fields supporting bias detection and explainability. The data structure enables robust analytics for both operational and regulatory use cases.

last updated 22 Jul 2025
by GoMask

The brief that made it

Training and validating AI models for automated credit decisioning

Sample rows

preview · 8 of 200 rows · all 30 columns

application_idstring	applicant_employment_statusstring	risk_scorefloat	fair_lending_flagboolean	loan_purposestring	application_datedate	applicant_idstring	applicant_first_namestring	applicant_last_namestring	applicant_birth_datedate	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_term_monthsinteger	loan_interest_ratefloat	loan_typestring	application_statusstring	decision_datedate	risk_categorystring	coapplicant_presentboolean	coapplicant_annual_incomefloat	coapplicant_credit_scoreinteger	decision_reasonstring
APP-000001	employed	0.13	false	home	2021-02-15	APPL-001	Olivia	Martinez	1992-08-03	female	married	78500	762	1232 Willow St	Houston	TX	77001	USA	245000	360	4.2	secured	approved	2021-02-19	low	true	65600	738	Strong credit and stable employment profile.
APP-000002	student	0.58	false	education	2020-09-30	APPL-002	Lucas	Berg	1999-01-12	male	single	1500	382	45 University Ave	Chicago	IL	60613	USA	8500	36	7.2	unsecured	approved	2020-10-08	medium	true	52100	803	Coapplicant provided and education loan.
APP-000003	self-employed	0.27	false	auto	2022-11-04	APPL-003	Mia	Fischer	1990-05-22	female	single	51000	695	8 Lindenstrasse	Berlin	blank	10437	DE	17500	60	5.6	secured	approved	2022-11-11	low	false	blank	blank	Good credit for auto loan.
APP-000004	retired	0.84	true	personal	2023-03-21	APPL-004	Ethan	Chen	1961-07-19	male	retired	9000	563	6 Orchard Rd	Singapore	blank	238841	SG	3000	24	9.3	unsecured	denied	2023-03-23	high	false	blank	blank	Low income and limited repayment capacity.
APP-000005	employed	0.43	false	personal	2024-01-10	APPL-005	Amelia	Dubois	1995-12-18	female	single	47300	687	32 Rue Lafayette	Paris	blank	75009	FR	12000	36	8.1	unsecured	approved	2024-01-15	medium	false	blank	blank	Applicant income sufficient for requested amount.
APP-000006	employed	0.09	false	home	2021-09-13	APPL-006	Priya	Singh	1983-10-02	female	married	124000	845	119 MG Road	Mumbai	MH	400001	IN	315000	240	5.1	secured	approved	2021-09-22	low	true	87000	769	High income and excellent credit.
APP-000007	self-employed	0.31	false	auto	2022-07-06	APPL-007	Jacob	Anderson	1971-11-10	male	divorced	38600	637	88 Queen St	Toronto	ON	M5C1S2	CA	17500	60	6.1	secured	approved	2022-07-15	medium	false	blank	blank	Stable self-employment history.
APP-000008	student	0.66	false	education	2020-12-01	APPL-008	Emma	Ivanova	2002-03-15	female	single	0	327	45 Prospekt Mira	Moscow	blank	129090	RU	4000	24	11.2	unsecured	approved	2020-12-09	medium	true	44800	692	Coapplicant with stable income.
APP-000009	employed	0.36	false	personal	2024-03-02	APPL-009	Sofia	Peterson	1998-04-11	female	single	41500	671	52 Main St	London	blank	W1H 4HQ	UK	10900	48	9.1	unsecured	approved	2024-03-07	medium	false	blank	blank	Moderate income and fair credit.
APP-000010	student	0.57	false	education	2023-10-19	APPL-010	Noah	Johnson	2001-10-22	male	single	0	338	77 College Rd	Dublin	blank	D02 K772	IE	15500	60	7.9	unsecured	approved	2023-10-22	medium	true	51200	720	Education loan with coapplicant support.
APP-000011	employed	0.17	false	auto	2022-04-17	APPL-011	Liam	Kim	1986-12-25	male	married	69500	781	17 Han St	Seoul	blank	04524	KR	31200	60	4.7	secured	approved	2022-04-23	low	false	blank	blank	Good income and excellent credit.
APP-000012	retired	0.91	true	personal	2020-06-11	APPL-012	Vera	Yamamoto	1958-09-08	female	widowed	11000	421	7 Chuo Dori	Tokyo	blank	104-0061	JP	4000	12	14.4	unsecured	denied	2020-06-15	high	false	blank	blank	Low income and weak credit history.
APP-000013	employed	0.11	false	home	2023-06-02	APPL-013	Sven	Lindgren	1982-03-16	male	married	96000	789	12 Vasagatan	Stockholm	blank	11120	SE	189000	180	3.8	secured	approved	2023-06-08	low	true	77000	728	Strong credit and income, home loan.
APP-000014	employed	0.33	false	auto	2024-04-01	APPL-014	Fatima	Ali	1997-05-29	female	single	48200	678	33 Zamalek St	Cairo	blank	11211	EG	15500	48	12.6	secured	approved	2024-04-10	medium	false	blank	blank	Sufficient income for auto loan.
APP-000015	employed	0.03	false	home	2023-12-05	APPL-015	Ava	Smith	1985-10-30	female	married	109000	829	207 Park Ave	New York	NY	10017	USA	375000	240	3.9	secured	approved	2023-12-12	low	true	92300	792	Excellent credit and high income.
APP-000016	self-employed	0.79	true	personal	2022-03-25	APPL-016	Daniel	Kowalski	1978-07-22	male	divorced	36800	597	25 Nowa St	Warsaw	blank	00-950	PL	9800	36	12.8	unsecured	denied	2022-03-29	high	false	blank	blank	Below average credit and self-employed risk.
APP-000017	student	0.61	false	education	2021-08-11	APPL-017	Sophia	Nguyen	2004-01-09	female	single	0	316	2 Tran Quoc Toan	Hanoi	blank	10000	VN	2200	18	10.5	unsecured	approved	2021-08-18	medium	true	29500	682	Education loan with coapplicant.
APP-000018	employed	0.19	false	auto	2022-05-30	APPL-018	Elena	Garcia	1993-02-01	female	married	54200	755	25 Calle Mayor	Madrid	blank	28013	ES	17500	48	4.8	secured	approved	2022-06-03	low	false	blank	blank	Strong credit, meets auto loan criteria.
APP-000019	employed	0.15	false	home	2020-03-11	APPL-019	Mason	Brown	1979-11-23	male	married	98500	792	14 King St	Sydney	NSW	2000	AU	134500	180	3.5	secured	approved	2020-03-19	low	true	61000	744	High income and strong credit for home loan.
APP-000020	self-employed	0.41	false	personal	2022-08-28	APPL-020	Isabella	Rossi	1996-09-19	female	single	42700	668	21 Via Roma	Rome	RM	00184	IT	21200	60	9.6	unsecured	approved	2022-09-02	medium	false	blank	blank	Income and credit adequate for personal loan.

What the 200 rows show

from the 200-row sample

Retired (applicant employment status) stands out: 29 of its 29 rows have fair_lending_flag = true, against 9 of 171 for the rest.

19%fair_lending_flag = true
0.29median risk_score
2loan types
2application statuses
3risk categories
4applicant genders

Fair lending flag rate by applicant_employment_statusfair_lending_flag = true

risk_score200 rows, in bands of 0.1

Every row with risk_score of 0.7 or more has fair_lending_flag = true. None below it.

loan_purpose200 rows · 6 values

personal64
auto52
home42
education26
other8
business8

30 columns by typefrom the column list below

string 17
integer 3
float 5
date 3
boolean 2

Columns

30 columns in four groups

blueprint · 30 columns
column	type	description	example
Text 17 columns
`application_id`	string	Unique identifier for each loan applicationunique	APP-000001
`applicant_id`	string	Unique identifier for the applicant	APPL-001
`applicant_first_name`	string	Applicant's first name	Olivia
`applicant_last_name`	string	Applicant's last name	Martinez
`applicant_gender`	string	Applicant's gendermale · female · other · prefer_not_to_say · optional	female
`applicant_marital_status`	string	Applicant's marital statussingle · married · divorced · widowed · other · optional	married
`applicant_employment_status`	string	Applicant's current employment statusemployed · self-employed · unemployed · student · retired	employed
`applicant_address_street`	string	Applicant's street addressoptional	1232 Willow St
`applicant_address_city`	string	Applicant's city of residenceoptional	Houston
`applicant_address_state`	string	Applicant's state or province of residenceoptional	TX
`applicant_address_postal_code`	string	Applicant's postal or ZIP codeoptional	77001
`applicant_address_country`	string	Applicant's country of residenceoptional	USA
`loan_purpose`	string	Purpose of the loan6 values	home
`loan_type`	string	Type of loan productsecured · unsecured · optional	secured
`application_status`	string	Final decision on the loan applicationapproved · denied	approved
`decision_reason`	string	Primary reason for approval or denialoptional	Meets all requirements
`risk_category`	string	Categorical risk profile assigned to the applicationlow · medium · high	low
Numbers 8 columns
`applicant_annual_income`	float	Applicant's annual income in local currency0 or more	78500
`applicant_credit_score`	integer	Applicant's credit score at the time of application300 to 900	762
`loan_amount`	float	Requested loan amount in local currency0 or more	245000
`loan_term_months`	integer	Requested loan term in months1 or more	360
`loan_interest_rate`	float	Interest rate offered for the loan (percentage, e.g., 5.5 for 5.5%)0 to 100	4.2
`risk_score`	float	Automated risk score assigned to the application (0-1 scale, where 1 is highest risk)0 to 1	0.13
`coapplicant_annual_income`	float	Co-applicant's annual income (if present)0 or more · optional	65600
`coapplicant_credit_score`	integer	Co-applicant's credit score (if present)300 to 900 · optional	738
Dates and times 3 columns
`application_date`	date	Date when the loan application was submitted	2021-02-15
`applicant_birth_date`	date	Applicant's date of birth	1992-08-03
`decision_date`	date	Date when the application decision was made	2021-02-19
True or false 2 columns
`coapplicant_present`	boolean	Indicates if a co-applicant is present on the application	true
`fair_lending_flag`	boolean	Indicates if the application was flagged for fair lending reviewoptional	false

Use it for

A finance dashboard
The fair_lending_flag rate, risk_score by applicant_employment_status and a breakdown of loan_purpose. Excel, Power BI or Tableau.
Why do 38 of 200 rows have fair_lending_flag = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications200
APP-0000010.13employed
APP-0000040.84retired
APP-0000120.91retired
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 rows30 columns

Yours1,000 rows30 columnsapplicant_address_street: UK only

blueprint · automated-loan-application-assessment

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

Credit score impacts approval probability
Income and debt ratios considered
Loan type affects risk profile
Applications with incomplete data flagged
Regulatory constraints enforced

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: automated-loan-application-assessment

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?