Finance
33 columns
200 rows
8 formats

Credit Risk Assessment Profiles

This dataset provides detailed profiles of credit applicants, including personal information, financial history, credit scores, loan application details, and risk assessments. It is designed to support financial institutions in evaluating creditworthiness, modeling risk, and making informed lending decisions. The dataset is ideal for predictive analytics, regulatory compliance, and portfolio management.

opened 9 times
last updated 20 Aug 2025
by GoMask

The brief that made it

Credit risk modeling and prediction

Sample rows

preview · 8 of 200 rows · all 33 columns

applicant_idstring	approval_statusstring	risk_assessment_scorefloat	default_historyboolean	address_countrystring	first_namestring	last_namestring	date_of_birthdate	genderstring	emailstring	phone_numberstring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	employment_statusstring	employer_namestring	annual_incomefloat	income_sourcestring	credit_scoreinteger	credit_history_length_yearsfloat	number_of_open_accountsinteger	number_of_closed_accountsinteger	total_outstanding_debtfloat	monthly_debt_paymentfloat	last_default_datedate	loan_amount_requestedfloat	loan_purposestring	application_datedate	risk_categorystring	approval_datedate	notesstring
A8UG_pLz	approved	0.18	false	USA	Sophia	Martinez	1992-03-15	female	sophia.martinez92@usamail.com	+1-212-555-0176	124 Elmwood Ave	New York	NY	10027	employed	TechNova Inc	87000	salary	728	8.2	4	2	23500.75	650	blank	35000	auto	2024-05-08	low	2024-05-11	Strong income and credit history, low risk.
jLkR2YwQ	approved	0.32	false	UK	Mohammed	Ali	1986-07-09	male	mohammed.ali86@londonmail.co.uk	+44 20 7946 0515	50 Parkside Road	London	Greater London	SW19 5AB	self-employed	Ali Consulting	152000	business	711	13.5	6	5	85000	2100	blank	60000	business	2024-05-10	medium	2024-05-13	Established self-employed, moderate risk due to debt.
n8S2vK_1	pending	0.68	false	India	Priya	Sharma	2002-11-24	female	priya.sharma02@mumuniv.in	+91-22-4995-3284	22 Juhu Lane	Mumbai	Maharashtra	400049	student	blank	0	other	384	0.9	0	0	0	0	blank	12000	education	2024-05-12	medium	blank	Student applicant, low income, limited credit history.
QpT7v6M2	approved	0.14	false	USA	Linda	Chen	1955-06-02	female	linda.chen55@retiremail.com	+1-303-555-0911	333 Aspen Ct	Denver	CO	80202	retired	blank	48000	pension	784	32.4	2	8	1200	80	blank	9000	personal	2024-05-03	low	2024-05-06	Retired, strong history, minimal debt.
W6fjR_2t	approved	0.26	false	Germany	Jonas	Müller	1978-12-18	male	jonas.mueller78@berlinmail.de	+49 30 5559 4412	17 Alexanderplatz	Berlin	Berlin	10178	employed	Berlin Finanz	72000	salary	680	15.7	5	3	42000	1200	blank	155000	home	2024-05-09	low	2024-05-13	Prime employed, home loan, good credit.
xL3R8wz9	pending	0.59	false	USA	Ava	Johnson	1999-09-05	female	ava.johnson99@usamail.com	+1-718-555-0284	89 Kings Hwy	Brooklyn	NY	11223	student	blank	6200	other	410	2.1	1	0	1800	110	blank	9500	education	2024-05-10	medium	blank	Education loan, student with low income.
gT8HxkW7	approved	0.21	false	Singapore	Ethan	Nguyen	1983-02-23	male	ethan.nguyen83@sgmail.sg	+65 6388 4192	102 Orchard Rd	Singapore	Singapore	238866	employed	SingTech	99000	salary	763	12.1	3	4	18000	700	blank	38000	auto	2024-05-05	low	2024-05-09	Excellent income and credit, auto loan.
Kq1G6eYB	approved	0.09	false	USA	Olivia	Green	1962-05-30	female	olivia.green62@retiremail.com	+1-718-555-0293	500 Silver Lake Blvd	Staten Island	NY	10309	retired	blank	32000	pension	795	29.7	2	7	0	0	blank	7000	personal	2024-05-11	low	2024-05-13	Retired, no debt, very low risk.
Z7cH4vKq	approved	0.27	false	USA	Lucas	Smith	1990-05-11	male	lucas.smith90@usamail.com	+1-312-555-0179	159 Clark St	Chicago	IL	60610	employed	Midwest Bank	69000	salary	670	10.9	4	2	25000	850	blank	27500	personal	2024-05-07	low	2024-05-10	Employed, personal loan, solid credit.
V8dF7hLp	pending	0.36	false	Mexico	Isabella	Garcia	1988-01-20	female	isabella.garcia88@mxmail.mx	+52 55 5598 4211	21 Paseo de la Reforma	Mexico City	CDMX	06500	employed	Grupo Constructora	57000	salary	642	7.8	3	1	16000	600	blank	22000	auto	2024-05-11	medium	blank	Moderate risk, auto loan, pending employment confirmation.
cP5B3hQ9	approved	0.23	false	USA	James	Murray	1970-08-30	male	james.murray70@usamail.com	+1-312-555-0191	11 Wacker Drive	Chicago	IL	60606	self-employed	Murray Partners	185000	business	746	22.1	7	6	94000	3100	blank	300000	business	2024-05-07	low	2024-05-11	Long history, high income, business expansion.
J7qL2vLr	approved	0.15	false	Poland	Marta	Kowalska	1995-04-12	female	marta.kowalska95@warsawmail.pl	+48 22 555 9872	200 Krakowskie Przedmiescie	Warsaw	Mazowieckie	00-927	employed	EuroBank	54000	salary	705	4.8	3	1	7400	400	blank	18000	personal	2024-05-06	low	2024-05-09	Young, employed, low risk, personal loan.
D9fH8kLm	approved	0.07	false	USA	Michael	Brown	1967-10-25	male	michael.brown67@usamail.com	+1-303-555-0157	77 Broadway	Denver	CO	80203	retired	blank	39000	pension	822	27.8	1	9	0	0	blank	12000	personal	2024-05-08	low	2024-05-10	Retired, no debt, excellent credit.
Y2tK9vQx	pending	0.34	false	Singapore	Emma	Lee	1993-08-01	female	emma.lee93@sgmail.sg	+65 6388 4918	76 Tanglin Rd	Singapore	Singapore	247964	employed	Green Logistics	56000	salary	658	7.2	3	2	14000	500	blank	21000	auto	2024-05-09	medium	blank	Auto loan, moderate risk, pending review.
Q4gL1wTz	approved	0.29	false	France	Frederic	Dubois	1980-12-03	male	frederic.dubois80@parismail.fr	+33 1 5559 4217	4 Rue de Rivoli	Paris	Île-de-France	75001	self-employed	Dubois Architecture	132000	business	723	16.2	7	5	72000	2700	blank	85000	business	2024-05-06	low	2024-05-10	Self-employed, business loan, moderate debt.
Fb7M8vR4	rejected	0.76	false	India	Ananya	Patel	2000-06-10	female	ananya.patel00@mumuniv.in	+91-22-4995-3285	55 Malabar Hill	Mumbai	Maharashtra	400006	student	blank	0	other	332	0.4	0	0	0	0	blank	15000	education	2024-05-12	high	2024-05-13	Rejected due to very short credit history.
Hf4D6vGh	pending	0.45	false	Mexico	Diego	Ramirez	1985-04-17	male	diego.ramirez85@mxmail.mx	+52 55 5598 4223	14 Calle Sur	Mexico City	CDMX	06760	self-employed	Ramirez Transport	98000	business	609	12.7	5	4	58000	1500	blank	42000	business	2024-05-11	medium	blank	Moderate risk, business loan, self-employed.
Z2jB6nLe	approved	0.13	false	USA	Amelia	King	1976-05-22	female	amelia.king76@usamail.com	+1-212-555-0192	22 E 54th St	New York	NY	10022	employed	King Design	102000	salary	793	18.4	4	5	18000	400	blank	34000	auto	2024-05-05	low	2024-05-09	High income, excellent credit, auto loan.
F4cJ7kLp	approved	0.06	false	USA	William	Evans	1968-03-02	male	william.evans68@usamail.com	+1-312-555-0185	77 N State St	Chicago	IL	60602	retired	blank	21000	pension	818	29.2	1	9	0	0	blank	4800	personal	2024-05-08	low	2024-05-11	Retired, small personal loan, excellent history.
X9bT2vQk	pending	0.41	false	Singapore	Noah	Kim	1997-01-29	male	noah.kim97@sgmail.sg	+65 6388 4745	19 Bukit Timah Rd	Singapore	Singapore	259701	employed	Kim Holdings	38000	salary	629	5.1	2	1	6500	270	blank	9000	personal	2024-05-06	medium	blank	Young employed, moderate risk, personal loan.

What the 200 rows show

from the 200-row sample

Rejected (approval status) stands out: 12 of its 16 rows have default_history = true, against 0 of 184 for the rest.

6%default_history = true
0.29median risk_assessment_score
2genders
3risk categories
5employment statuses
5income sources

Default history rate by approval_statusdefault_history = true

Rejected (approval status)'s mean risk_assessment_score is 0.83, against 0.21 for approved and 0.47 for pending.

risk_assessment_score200 rows, in bands of 0.1

Median 0.29, from 0.04 to 0.95.

address_country200 rows · top 10 of 35 values

USA43
UK18
India15
Russia15
Germany13
Mexico9
France8
Canada7
China6
Singapore5

33 columns by typefrom the column list below

string 18
integer 4
float 6
date 4
boolean 1

Columns

33 columns in four groups

blueprint · 33 columns
column	type	description	example
Text 18 columns
`applicant_id`	string	Unique identifier for each credit applicantunique	A8UG_pLz
`first_name`	string	Applicant's first name	Sophia
`last_name`	string	Applicant's last name	Martinez
`gender`	string	Applicant's gendermale · female · other · prefer_not_to_say · optional	female
`email`	string	Applicant's email addressunique · optional	ethan.nguyen83@sgmail.sg
`phone_number`	string	Applicant's phone numberoptional	+1-212-555-0176
`address_street`	string	Applicant's street address	124 Elmwood Ave
`address_city`	string	Applicant's city	New York
`address_state`	string	Applicant's state or province	NY
`address_postal_code`	string	Applicant's postal code	10027
`address_country`	string	Applicant's country of residence	USA
`employment_status`	string	Applicant's current employment statusemployed · self-employed · unemployed · retired · student	employed
`employer_name`	string	Name of the applicant's employer (if employed)optional	TechNova Inc
`income_source`	string	Primary source of incomesalary · business · pension · investment · other	salary
`loan_purpose`	string	Purpose of the requested loan6 values	auto
`risk_category`	string	Categorized risk level based on assessment scorelow · medium · high	low
`approval_status`	string	Final status of the loan applicationapproved · pending · rejected	approved
`notes`	string	Additional notes or comments from the credit officeroptional	Strong income and credit …
Numbers 10 columns
`annual_income`	float	Applicant's annual income in local currency0 or more	87000
`credit_score`	integer	Applicant's current credit score300 to 900	728
`credit_history_length_years`	float	Number of years of credit history0 or more	8.2
`number_of_open_accounts`	integer	Number of currently open credit accounts0 or more	4
`number_of_closed_accounts`	integer	Number of previously closed credit accounts0 or more	2
`total_outstanding_debt`	float	Total outstanding debt across all accounts0 or more	23500.75
`monthly_debt_payment`	float	Total monthly debt payment obligation0 or more	650
`number_of_defaults`	integer	Number of recorded defaults in applicant's history0 or more · optional	0
`loan_amount_requested`	float	Amount of loan requested by applicant0 or more	35000
`risk_assessment_score`	float	Calculated risk score for the applicant (0-1, where 1 is highest risk)0 to 1	0.18
Dates and times 4 columns
`date_of_birth`	date	Applicant's date of birth	1992-03-15
`last_default_date`	date	Date of most recent default (if any)optional	2023-11-15
`application_date`	date	Date of loan application	2024-05-08
`approval_date`	date	Date when application was approved or rejectedoptional	2024-05-11
True or false 1 column
`default_history`	boolean	Indicates if the applicant has any history of loan default	false

Use it for

A finance dashboard
The default_history rate, risk_assessment_score by approval_status and a breakdown of address_country. Excel, Power BI or Tableau.
Why do 12 of 200 rows have default_history = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applicants200
A8UG_pLz0.18approved
J2gK8vLp0.87rejected
P3gK7vLr0.79rejected
A software demo
Believable applicants with first_name, last_name and date_of_birth to fill a screen in front of a buyer.

This dataset200 rows33 columns

Yours1,000 rows33 columnsaddress_street: UK only

blueprint · credit-risk-assessment-profiles

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 33 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 income, employment, and debt data
Categorize applicants by risk tiers
Exclude incomplete or fraudulent applications
Track outcomes of credit approvals

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: credit-risk-assessment-profiles

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

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