Finance
28 columns
201 rows
8 formats

Finance Customer Risk Profiles

This dataset provides detailed synthetic customer risk profiles for financial institutions, including demographic information, financial activity metrics, risk scores, and compliance indicators. It is ideal for developing and testing lending analytics, risk modeling, and anti-money laundering (AML) compliance systems in banking environments.

last updated 29 Jul 2025
by GoMask

The brief that made it

Credit risk modeling and customer segmentation

Sample rows

preview · 8 of 201 rows · all 28 columns

customer_idstring	risk_categorystring	credit_scoreinteger	is_pepboolean	account_statusstring	first_namestring	last_namestring	date_of_birthdate	genderstring	emailstring	phone_numberstring	street_addressstring	citystring	statestring	postal_codestring	countrystring	account_open_datedate	annual_incomefloat	employment_statusstring	risk_scorefloat	num_active_accountsinteger	total_account_balancefloat	num_loansinteger	total_loan_amountfloat	num_credit_cardsinteger	avg_monthly_spendingfloat	last_transaction_datedatetime	is_sanctionedboolean
JH_Abc12	low	765	false	active	Isabella	Nguyen	1993-03-05	female	isabella.nguyen93@outlook.com	+447912345678	28 Queen Street	London	England	SW1A1AA	GB	2011-07-15	54000	employed	0.18	2	13500.5	1	8200	2	1150.5	2024-05-31T10:22:37	false
RZLU_983	low	791	true	active	Derek	Owens	1978-10-22	male	derek.owens78@gmail.com	+16135550123	4305 Pine Ridge Rd	Miami	FL	33176	US	2005-04-19	121000	self-employed	0.11	4	87200.2	2	43000	5	12500	2024-06-11T14:47:02	false
qTw_7214	medium	604	false	active	Yusuf	Farah	2001-01-17	male	yusuf.farah01@outlook.com	+971551234567	Al Maktoum St 45	Dubai	Dubai	00000	AE	2020-10-02	15500	student	0.33	1	700	0	0	0	120	2024-06-13T16:43:12	false
K8ZxV_1	low	721	false	active	Rebecca	Tan	1982-09-11	female	rebeccatan82@gmail.com	+6565238112	12 Lorong 7	Singapore	Central	310012	SG	2008-02-21	84000	employed	0.29	2	17500	1	12000	2	1950	2024-06-10T08:20:41	false
mQx_Wi53	medium	639	false	retired	Carlos	Hernandez	1956-05-29	male	carlos.hernandez56@yahoo.com	+525555123456	Av. Insurgentes 1002	Mexico City	CDMX	03100	MX	1993-11-14	15600	retired	0.41	1	2500	0	0	0	270	2023-12-17T12:05:53	false
Fv88l_6	low	704	false	active	Ava	Smith	1997-01-02	female	ava.smith97@icloud.com	+14165552306	199 Bay Street	Toronto	ON	M5J2J5	CA	2017-03-14	47000	employed	0.17	2	8700	1	3700	1	1021.4	2024-06-14T11:33:10	false
BK_12x9	low	718	false	active	Alexei	Volkov	1969-12-19	male	alexei.volkov69@yandex.ru	+74951234567	Prospekt Mira 53	Moscow	Moscow	129110	RU	1990-06-28	99000	self-employed	0.25	3	23480	1	16000	3	2400	2024-06-12T15:57:21	false
VxR_8123	low	782	false	active	Priya	Singh	1988-08-09	female	priya.singh88@gmail.com	+911144556677	17 MG Road	Delhi	Delhi	110001	IN	2013-09-17	73200	employed	0.09	2	11100	1	5100	2	1570	2024-06-13T18:21:00	false
rZL_Abc1	low	763	false	active	Fatima	Al Farsi	1992-04-22	female	fatima.alfarsi92@adib.ae	+971502334455	Corniche Rd 150	Abu Dhabi	Abu Dhabi	00000	AE	2016-11-28	67000	employed	0.19	2	21000	1	16000	2	1610	2024-06-12T08:01:17	false
XkL_1892	low	786	false	active	Jasper	Chen	1985-06-02	male	jasper.chen85@mail.com	+85291234567	88 Nathan Rd	Hong Kong	Hong Kong	00000	HK	2006-10-15	97000	employed	0.12	3	38000	2	47000	3	10120	2024-06-14T13:19:09	false
LNg_2x19	low	747	false	active	Sophie	Lemaire	1998-05-14	female	sophie.lemaire98@wanadoo.fr	+33612345678	5 Rue de Rivoli	Paris	Ile-de-France	75001	FR	2018-06-03	51000	employed	0.22	2	9700	0	0	1	980	2024-06-13T09:59:51	false
QwS_2022	high	336	false	active	Mohammed	Mahmoud	2003-09-09	male	mohammed.mahmoud03@gmail.com	+962799123456	King Abdullah St 77	Amman	Amman	11181	JO	2021-02-12	1200	student	0.65	1	90	0	0	0	30	2024-06-13T11:23:54	false
WbX_1904	low	742	false	active	Olivia	Johnson	1987-12-12	female	olivia.johnson87@yahoo.com	+12125678901	400 Central Park S	New York	NY	10019	US	2009-07-08	85000	employed	0.23	2	25600	1	9000	2	1830	2024-06-14T12:41:38	false
Rt_2488	low	758	false	active	David	Kim	1995-03-03	male	david.kim95@naver.com	+821012345678	123 Gangnam-daero	Seoul	Seoul	06035	KR	2013-11-17	57000	employed	0.16	2	14200	1	5000	2	1240	2024-06-13T07:58:24	false
aeP_7xQ3	low	729	false	active	Emily	Brown	1973-04-27	female	emily.brown73@gmail.com	+61412345678	35 Collins St	Melbourne	VIC	3000	AU	1997-05-11	119000	self-employed	0.14	3	31200	2	27000	3	8200	2024-06-14T16:22:03	false
jTz_827	low	741	false	active	Lucas	Garcia	1990-11-01	male	lucas.garcia90@hotmail.com	+34912345678	Calle Mayor 10	Madrid	Madrid	28013	ES	2012-09-20	65000	employed	0.21	2	17000	1	8000	2	1180	2024-06-13T10:44:41	false
rK1_7281	low	753	false	active	Mia	Williams	1999-02-21	female	mia.williams99@gmail.com	+16475550199	520 King St W	Toronto	ON	M5V1L7	CA	2018-07-15	52000	employed	0.13	2	9500	0	0	1	1050	2024-06-13T15:08:57	false
H3q_Pt99	low	809	false	active	Arjun	Sharma	1984-07-04	male	arjun.sharma84@icici.in	+919876543210	Plot 44, Sector 15	Gurgaon	Haryana	122001	IN	2007-11-03	130000	employed	0.07	3	51000	2	23000	4	13100	2024-06-14T10:43:59	false
TzR_9v81	high	398	false	active	Samantha	Evans	2005-05-18	female	sam.evans05@icloud.com	+15145551212	800 Main St	Austin	TX	78701	US	2022-01-27	2500	student	0.67	1	80.5	0	0	0	25	2024-06-13T19:09:33	false
YtX_5p77	medium	612	false	retired	Linda	Berg	1952-11-30	female	linda.berg52@gmail.com	+46701234567	Storgatan 19	Stockholm	Stockholm	11356	SE	1972-06-10	9100	retired	0.32	1	700	0	0	0	160	2023-12-06T09:44:14	false

What the 201 rows show

from the 201-row sample

High (risk category) stands out: mean credit_score is 420.7, against 695.6 for the rest.

5%is_pep = true
720median credit_score
3genders
4employment statuses
54countries
43,120median annual_income

Mean credit_score by risk_category201 rows

credit_score201 rows, in bands of 100

Median 720, from 302 to 836.

account_status201 rows · 4 values

active172
retired14
inactive13
closed2

28 columns by typefrom the column list below

string 14
integer 4
float 5
date 2
datetime 1
boolean 2

Columns

28 columns in four groups

blueprint · 28 columns
column	type	description	example
Text 14 columns
`customer_id`	string	Unique identifier for each customer profileunique	JH_Abc12
`first_name`	string	Customer's first name	Isabella
`last_name`	string	Customer's last name	Nguyen
`gender`	string	Customer's gendermale · female · other · prefer_not_to_say · optional	female
`email`	string	Customer's email addressunique	derek.owens78@gmail.com
`phone_number`	string	Customer's primary phone numberoptional	+447912345678
`street_address`	string	Customer's street addressoptional	28 Queen Street
`city`	string	Customer's city of residenceoptional	London
`state`	string	Customer's state or provinceoptional	England
`postal_code`	string	Customer's postal or ZIP codeoptional	SW1A1AA
`country`	string	Customer's country of residenceoptional	GB
`account_status`	string	Current status of the customer's primary accountactive · inactive · closed · suspended	active
`employment_status`	string	Customer's current employment status6 values · optional	employed
`risk_category`	string	Categorical risk label based on risk_scorelow · medium · high · critical	low
Numbers 9 columns
`annual_income`	float	Customer's self-reported or estimated annual income in USD0 or more	54000
`credit_score`	integer	Customer's most recent credit score300 to 850	765
`risk_score`	float	Synthetic risk score for lending and compliance analytics (0.0 = lowest risk, 1.0 = highest risk)0 to 1	0.18
`num_active_accounts`	integer	Number of currently active bank accounts held by the customer0 or more	2
`total_account_balance`	float	Total balance across all customer accounts in USD0 or more	13500.5
`num_loans`	integer	Number of active loans held by the customer0 or more	1
`total_loan_amount`	float	Total outstanding loan amount in USD0 or more	8200
`num_credit_cards`	integer	Number of credit cards held by the customer0 or more	2
`avg_monthly_spending`	float	Average monthly spending across all accounts in USD0 or more	1150.5
Dates and times 3 columns
`date_of_birth`	date	Customer's date of birth	1993-03-05
`account_open_date`	date	Date when the customer opened their primary bank account	2011-07-15
`last_transaction_date`	datetime	Timestamp of the customer's most recent financial transactionoptional	2024-05-31T10:22:37
True or false 2 columns
`is_pep`	boolean	Indicates if the customer is a Politically Exposed Person (PEP)	false
`is_sanctioned`	boolean	Indicates if the customer appears on a sanctions list	false

Use it for

A finance dashboard
The is_pep rate, credit_score by risk_category and a breakdown of account_status. Excel, Power BI or Tableau.
Why do the 21 high rows have a mean credit_score of 420.7?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Customers201
JH_Abc12765low
RZLU_983791low
LmX_9217779low
A software demo
Believable customers with first_name, last_name and date_of_birth to fill a screen in front of a buyer.

This dataset201 rows28 columns

Yours1,000 rows28 columnsstreet_address: UK only

blueprint · finance-customer-risk-profiles

Behind this dataset

Same schema. As many rows as you need.

These 201 rows came out of a blueprint — 28 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

Each profile includes demographic fields
Risk score derived from account activity
High-risk profiles flagged for review
Credit utilization ratio included

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-customer-risk-profiles

What should your data show?

No signup. No card.

Sample rows

What the 201 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?