Finance
39 columns
140 rows
8 formats

Loan Application Risk Analysis

This dataset provides a comprehensive view of loan applications, capturing applicant demographics, financial profiles, loan details, and subsequent loan performance. Designed for credit risk modeling and decision automation, it enables in-depth analysis of risk factors, default prediction, and portfolio management in the lending industry.

last updated 12 Jul 2025
by GoMask

The brief that made it

Credit risk modeling and default prediction

Sample rows

preview · 8 of 140 rows · all 39 columns

application_idstring	loan_purposestring	loan_term_monthsinteger	default_flagboolean	loan_statusstring	applicant_idstring	application_datedate	loan_amountfloat	interest_ratefloat	application_statusstring	decision_datedate	applicant_first_namestring	applicant_last_namestring	applicant_date_of_birthdate	applicant_genderstring	applicant_marital_statusstring	applicant_employment_statusstring	applicant_annual_incomefloat	applicant_credit_scoreinteger	applicant_emailstring	applicant_phone_numberstring	applicant_street_addressstring	applicant_citystring	applicant_statestring	applicant_postal_codestring	applicant_countrystring	num_dependentsinteger	existing_debtfloat	monthly_expensesfloat	collateral_typestring	collateral_valuefloat	coapplicant_idstring	loan_disbursed_datedate	loan_repaid_datedate	num_payments_madeinteger	num_payments_missedinteger	last_payment_datedate
APP00001	home	180	false	active	USR10001	2023-02-14	245800	5.2	approved	2023-02-16	Michael	Reynolds	1982-05-21	male	married	employed	121500	815	mreynolds@usmail.com	+1-415-555-0137	827 Maple Ave	San Francisco	CA	94107	USA	2	18500	3200	property	310000	blank	2023-02-17	blank	14	0	2024-03-25
APP00002	auto	24	false	active	USR10002	2023-01-10	9700	9.6	approved	2023-01-12	Priya	Menon	1995-03-15	female	single	employed	54800	684	priyamenon@inmail.in	+91-9876543210	17 MG Road	Bangalore	KA	560001	India	0	3200	1700	vehicle	11200	blank	2023-01-13	blank	12	1	2024-01-14
APP00003	personal	36	false	blank	USR10003	2024-04-21	14200	18.5	pending	blank	Jose	Martinez	1990-08-29	male	single	self-employed	29800	502	jmartinez@mxmail.mx	+52-55-2345-6789	Calle 12 #56	Mexico City	CDMX	01000	Mexico	1	2900	1350	none	0	blank	blank	blank	0	0	blank
APP00004	home	240	false	active	USR10004	2022-11-18	370000	4.9	approved	2022-11-19	Anna	Sokolova	1974-10-11	female	married	self-employed	187000	799	asokolova@ru-mail.ru	+7-495-765-4321	Lenina 14	Moscow	MOW	101000	Russia	3	47000	4500	property	410000	USR10005	2022-11-21	blank	28	0	2024-03-10
APP00005	home	240	false	active	USR10005	2022-11-18	370000	4.9	approved	2022-11-19	Ivan	Petrov	1972-03-03	male	married	self-employed	205000	785	ipetrov@ru-mail.ru	+7-495-987-6543	Lenina 14	Moscow	MOW	101000	Russia	3	47000	4500	property	410000	USR10004	2022-11-21	blank	28	0	2024-03-10
APP00006	education	12	false	blank	USR10006	2023-07-12	3900	22.7	pending	blank	Sophie	Nguyen	2003-09-15	female	single	student	0	188	sophien@viexample.vn	blank	22 Le Loi	Ho Chi Minh	SG	700000	Vietnam	0	0	640	none	0	blank	blank	blank	0	0	blank
APP00007	auto	48	false	active	USR10007	2022-12-10	55000	8.7	approved	2022-12-13	James	Wilson	1987-12-14	male	single	employed	68300	704	jwilson@usmail.com	+1-212-555-0199	130 E 3rd St	New York	NY	10003	USA	0	7000	1850	vehicle	23500	blank	2022-12-15	blank	35	1	2024-03-15
APP00008	personal	18	false	closed	USR10008	2022-09-13	7200	21.9	approved	2022-09-15	Linda	Kowalski	1998-04-23	female	single	employed	23400	530	lindak@plmail.pl	+48-22-123-4567	ul. Sienna 6	Warsaw	MZ	00-121	Poland	0	3900	1150	none	0	blank	2022-09-16	2024-03-01	18	0	2024-03-01
APP00009	personal	9	false	blank	USR10009	2023-05-22	2300	28.3	rejected	2023-05-25	Rahul	Jain	2001-10-02	male	single	student	0	98	rahuljain@inmail.in	blank	9 Tilak Marg	Delhi	DL	110001	India	0	0	540	none	0	blank	blank	blank	0	0	blank
APP00010	business	60	false	active	USR10010	2022-10-05	98000	11.3	approved	2022-10-08	Frederik	Jensen	1979-12-11	male	married	self-employed	142000	753	fjensen@dkmail.dk	+45-22-123-456	Vesterbrogade 58	Copenhagen	84	1620	Denmark	2	24500	3450	property	135000	blank	2022-10-10	blank	54	2	2024-03-18
APP00011	other	6	false	active	USR10011	2024-01-26	3300	43.5	approved	2024-01-27	Omar	El-Sayed	2000-06-14	male	single	unemployed	0	113	omarsayed@egmail.eg	blank	6 Ramsis St	Cairo	C	11311	Egypt	0	0	350	none	0	blank	2024-01-29	blank	2	1	2024-03-01
APP00012	auto	36	false	active	USR10012	2023-05-19	25000	10.1	approved	2023-05-21	Sarah	Taylor	1993-09-18	female	single	employed	61000	672	staylor@usmail.com	+1-646-555-0148	729 8th Ave	New York	NY	10019	USA	0	8100	1680	vehicle	15500	blank	2023-05-23	blank	10	0	2024-03-05
APP00013	home	300	false	active	USR10013	2022-05-03	505000	3.4	approved	2022-05-06	Emily	Smith	1985-01-17	female	married	employed	145000	799	emilysmith@usmail.com	+1-312-555-0178	3918 Clark St	Chicago	IL	60613	USA	2	28000	4100	property	560000	blank	2022-05-09	blank	23	0	2024-03-02
APP00014	education	24	false	blank	USR10014	2024-03-09	5900	18.2	pending	blank	Elena	Popova	2004-11-20	female	single	student	0	77	elenapopova@ru-mail.ru	blank	Nevsky 112	St Petersburg	LEN	190000	Russia	0	0	570	none	0	blank	blank	blank	0	0	blank
APP00015	personal	36	false	active	USR10015	2023-06-11	19800	23.9	approved	2023-06-13	Kofi	Addo	1992-06-20	male	single	employed	27800	561	kofiaddo@ghmail.gh	+233-20-555-0017	12 Liberation Rd	Accra	AA	00233	Ghana	1	2100	990	none	0	blank	2023-06-14	blank	10	1	2024-03-01
APP00016	business	120	false	active	USR10016	2022-08-18	264000	13.4	approved	2022-08-22	Chloe	Martin	1975-11-30	female	married	self-employed	220000	775	cmartin@frmail.fr	+33-1-2345-6789	23 Rue de Rivoli	Paris	IDF	75004	France	2	49000	5200	property	270000	blank	2022-08-24	blank	19	0	2024-03-15
APP00017	personal	10	false	active	USR10017	2023-12-22	4200	27.5	approved	2023-12-23	Diego	Gomez	1996-02-11	male	single	unemployed	0	214	diegogomez@mxmail.mx	blank	Avenida Juarez 123	Guadalajara	JAL	44100	Mexico	0	0	840	none	0	blank	2023-12-27	blank	2	2	2024-03-01
APP00018	home	300	false	active	USR10018	2024-02-06	520000	3.9	approved	2024-02-07	Samantha	Johnson	1981-07-30	female	married	employed	148700	808	sjohnson@usmail.com	+1-303-555-0112	1200 16th St	Denver	CO	80202	USA	3	32000	4100	property	560000	blank	2024-02-10	blank	2	0	2024-03-01
APP00019	business	60	false	active	USR10019	2023-08-21	64000	14.7	approved	2023-08-23	Lukas	Keller	1982-03-12	male	married	self-employed	126000	729	lkeller@demail.de	+49-30-123-4567	Friedrichstr. 15	Berlin	BE	10117	Germany	2	24500	3200	property	67000	blank	2023-08-25	blank	7	0	2024-03-02
APP00020	personal	8	false	blank	USR10020	2023-10-02	4100	27.8	rejected	2023-10-05	Lisa	Nguyen	1999-05-27	female	single	unemployed	0	101	lisanguyen@viexample.vn	blank	58 Tran Hung Dao	Hanoi	HN	100000	Vietnam	0	0	580	none	0	blank	blank	blank	0	0	blank

What the 140 rows show

from the 140-row sample

Home (loan purpose) stands out: mean loan_term_months is 241.9, against 29.5 for the rest.

4%default_flag = true
30.5median loan_term_months
3applicant genders
4collateral types
5applicant marital statuses
5applicant employment statuses

Mean loan_term_months by loan_purpose140 rows

loan_term_months140 rows, in bands of 50

Median 30.5, from 3 to 360.

loan_status109 rows with a value · 31 left blank

active96
closed6
defaulted3
charged_off2
in_arrears2

39 columns by typefrom the column list below

string 19
integer 5
float 8
date 6
boolean 1

Columns

39 columns in four groups

blueprint · 39 columns
column	type	description	example
Text 19 columns
`application_id`	string	Unique identifier for each loan applicationunique	APP00001
`applicant_id`	string	Unique identifier for each applicant	USR10001
`loan_purpose`	string	Purpose for which the loan is requested (e.g., home, auto, education, personal)6 values	home
`application_status`	string	Current status of the loan applicationpending · approved · rejected · withdrawn	approved
`applicant_first_name`	string	Applicant's first name	Michael
`applicant_last_name`	string	Applicant's last name	Reynolds
`applicant_gender`	string	Applicant's gendermale · female · other · prefer_not_to_say · optional	male
`applicant_marital_status`	string	Applicant's marital status6 values · optional	married
`applicant_employment_status`	string	Applicant's current employment status6 values	employed
`applicant_email`	string	Applicant's email address	mreynolds@usmail.com
`applicant_phone_number`	string	Applicant's phone numberoptional	+1-415-555-0137
`applicant_street_address`	string	Applicant's street address	827 Maple Ave
`applicant_city`	string	Applicant's city of residence	San Francisco
`applicant_state`	string	Applicant's state or province of residence	CA
`applicant_postal_code`	string	Applicant's postal or ZIP code	94107
`applicant_country`	string	Applicant's country of residence	USA
`collateral_type`	string	Type of collateral provided, if anynone · property · vehicle · savings · other · optional	property
`coapplicant_id`	string	Unique identifier for a co-applicant, if presentoptional	USR10005
`loan_status`	string	Current status of the loanactive · closed · defaulted · charged_off · in_arrears · optional	active
Numbers 13 columns
`loan_amount`	float	Total amount of loan requested by the applicant0 or more	245800
`loan_term_months`	integer	Duration of the loan in months1 or more	180
`interest_rate`	float	Annual interest rate applied to the loan0 or more	5.2
`applicant_annual_income`	float	Applicant's annual income in local currency0 or more	121500
`applicant_credit_score`	integer	Applicant's credit score at the time of application0 to 1,000	815
`num_dependents`	integer	Number of dependents the applicant supports0 or more · optional	2
`existing_debt`	float	Total value of the applicant's existing debts0 or more · optional	18500
`monthly_expenses`	float	Applicant's average monthly expenses0 or more · optional	3200
`collateral_value`	float	Estimated value of the collateral provided0 or more · optional	310000
`num_payments_made`	integer	Number of payments made towards the loan0 or more · optional	14
`num_payments_missed`	integer	Number of missed or late payments0 or more · optional	0
`loss_given_default`	float	Estimated loss given default as a percentage (0-100)0 to 100 · optional	0
`recovery_amount`	float	Amount recovered after default, if applicable0 or more · optional	0
Dates and times 6 columns
`application_date`	date	Date when the loan application was submitted	2023-02-14
`decision_date`	date	Date when the application was approved or rejectedoptional	2023-02-16
`applicant_date_of_birth`	date	Applicant's date of birth	1982-05-21
`loan_disbursed_date`	date	Date when the loan was disbursed to the applicantoptional	2023-02-17
`loan_repaid_date`	date	Date when the loan was fully repaidoptional	2024-03-01
`last_payment_date`	date	Date of the most recent payment madeoptional	2024-03-25
True or false 1 column
`default_flag`	boolean	Indicates whether the loan has defaulted (true/false)optional	false

Use it for

A finance dashboard
The default_flag rate, loan_term_months by loan_purpose and a breakdown of loan_status. Excel, Power BI or Tableau.
Why do the 31 home rows have a mean loan_term_months of 241.9?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications140
APP00001180home
APP000396personal
APP000456personal
A software demo
Believable applications with applicant_id, application_date and loan_amount to fill a screen in front of a buyer.

This dataset140 rows39 columns

Yours1,000 rows39 columnsapplicant_street_address: UK only

blueprint · loan-application-risk-analysis

Behind this dataset

Same schema. As many rows as you need.

These 140 rows came out of a blueprint — 39 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

Applications missing required documents are rejected
Applicants with credit score <600 are high risk
Loan approvals require verified income
Delinquent loans are flagged after 30 days overdue

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-risk-analysis

What should your data show?

No signup. No card.

Sample rows

What the 140 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?