Finance
30 columns
700 rows
8 formats

Credit Card Risk Assessment

This dataset provides a comprehensive view of credit card applications and account management, including customer demographics, credit limits, utilization rates, payment behaviors, rewards tracking, and risk-based pricing. It is ideal for risk modeling, customer segmentation, and financial product optimization in the banking and credit industry.

opened 10 times
last updated 1 Nov 2025
by GoMask

The brief that made it

Credit risk modeling and scoring

Sample rows

preview · 8 of 700 rows · all 30 columns

application_idstring	pricing_tierstring	risk_scorefloat	account_statusstring	customer_idstring	application_datedate	account_idstring	account_open_datedate	credit_limitfloat	current_balancefloat	utilization_ratefloat	payment_due_datedate	last_payment_datedate	last_payment_amountfloat	minimum_payment_duefloat	payment_statusstring	rewards_points_balanceinteger	rewards_points_earnedinteger	rewards_points_redeemedinteger	annual_percentage_ratefloat	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	date_of_birthdate	employment_statusstring	annual_incomefloat	emailstring	phone_numberstring
APP100001	prime	0.28	active	CUST1001	2023-09-14	ACC100001	2023-09-17	7500	2550	34	2023-10-17	2023-10-15	850	127.5	on-time	1560	320	0	15.4	431 Oak Ridge Ave	Cleveland	OH	44109	USA	1990-07-22	employed	56500	ashley.r.jones90@gmail.com	+1-216-555-0933
APP100002	prime	0.21	active	CUST1002	2023-06-04	ACC100002	2023-06-10	4200	1320	31.43	2023-07-10	2023-07-07	350	52.8	on-time	860	120	0	18.2	28B Cambridge St	Boston	MA	02134	USA	1992-02-10	employed	48900	michael.smith92@outlook.com	+1-857-555-3271
APP100003	super-prime	0.08	closed	CUST1003	2023-11-02	ACC100003	2023-11-03	4800	0	0	2023-12-03	2023-12-01	220	0	on-time	0	0	0	8.2	17 Queen's Walk	London	England	SW1A 2AA	UK	1953-03-19	retired	8700	david.evans1953@btinternet.com	+44 20 7946 1812
APP100004	subprime	0.93	pending	CUST1004	2024-02-01	blank	blank	0	0	0	2024-03-01	blank	blank	0	missed	0	0	0	31.5	47 Leninsky Prospekt	Moscow	Moscow	119334	Russia	2000-10-03	student	3400	katya.ivanova00@yandex.ru	+7-495-555-7721
APP100005	prime	0.33	active	CUST1005	2023-05-17	ACC100005	2023-05-22	11500	3450	30	2023-06-22	2023-06-20	950	172.5	on-time	2140	410	120	16.9	9 King St West	Toronto	ON	M5H 1A1	Canada	1984-04-04	employed	79400	sophia.chan84@mail.ca	+1-416-555-6214
APP100006	prime	0.19	active	CUST1006	2022-08-20	ACC100006	2022-08-22	32000	9600	30	2022-09-22	2022-09-21	1500	480	on-time	3400	600	0	14.6	12 Orchard Lane	Singapore	Singapore	238841	Singapore	1979-06-30	employed	127000	li.wei79@singnet.sg	+65-6222-5541
APP100007	super-prime	0.11	active	CUST1007	2022-11-14	ACC100007	2022-11-15	25000	1800	7.2	2022-12-15	2022-12-13	600	36	on-time	5700	740	320	7.3	81 Rue du Bac	Paris	Île-de-France	75007	France	1957-09-10	retired	0	marie.dupont57@orange.fr	+33 1 4255 8811
APP100008	prime	0.17	active	CUST1008	2023-03-12	ACC100008	2023-03-18	4800	960	20	2023-04-18	2023-04-17	200	48	on-time	420	90	0	13.1	77 Avenida Paulista	São Paulo	SP	01311-000	Brazil	1997-12-08	employed	29500	joao.lima97@uol.com.br	+55 11 3636-8181
APP100009	subprime	0.96	pending	CUST1009	2024-01-15	blank	blank	0	0	0	2024-02-15	blank	blank	0	missed	0	0	0	33.4	12 Longmarket St	Cape Town	Western Cape	8001	South Africa	2004-02-15	unemployed	0	thando.ndlovu04@webmail.co.za	+27 21 555 7092
APP100010	prime	0.38	active	CUST1010	2023-10-21	ACC100010	2023-10-29	9800	3920	40	2023-11-29	2023-11-27	1150	196	on-time	1850	240	0	17.2	14 Madison Ave	New York	NY	10016	USA	1988-11-12	employed	69000	james.wilson88@gmail.com	+1-212-555-3184
APP100011	subprime	0.56	active	CUST1011	2023-07-05	ACC100011	2023-07-08	325000	212500	65.38	2023-08-08	2023-08-06	12000	10625	late	82000	9400	12000	29.1	31 Wall St	New York	NY	10005	USA	1971-03-22	employed	475000	emma.thompson71@citibank.com	+1-212-555-4410
APP100012	subprime	0.89	suspended	CUST1012	2022-12-09	ACC100012	2022-12-10	6200	4514	72.81	2023-01-10	2023-01-02	0	225.7	missed	0	0	0	34.8	5 Rua Augusta	Lisbon	Lisboa	1100-053	Portugal	1998-06-16	student	6200	joana.silva98@sapo.pt	+351 21 555 1234
APP100013	prime	0.19	active	CUST1013	2023-05-23	ACC100013	2023-05-30	48000	9600	20	2023-06-30	2023-06-28	2100	960	on-time	3700	550	0	16.1	22 Friedrichstrasse	Berlin	Berlin	10117	Germany	1982-09-17	employed	84500	hans.meyer82@gmail.de	+49 30 555 7711
APP100014	super-prime	0.12	closed	CUST1014	2022-04-30	ACC100014	2022-05-03	15000	0	0	2022-06-03	2022-06-01	450	0	on-time	0	0	0	7.9	104 Market St	San Francisco	CA	94105	USA	1959-05-15	retired	0	nancy.green59@att.net	+1-415-555-1236
APP100015	prime	0.23	active	CUST1015	2023-02-12	ACC100015	2023-02-17	6200	1860	30	2023-03-17	2023-03-16	560	62	on-time	780	150	0	16.5	190 Yonge St	Toronto	ON	M5B 2H1	Canada	1991-10-21	employed	50700	lucas.martin91@bell.ca	+1-647-555-8182
APP100016	subprime	0.87	active	CUST1016	2022-10-25	ACC100016	2022-10-27	5400	3780	70	2022-11-27	2022-11-22	0	189	late	0	0	0	32.9	65 Via Roma	Rome	Lazio	00184	Italy	1995-03-12	student	4100	giulia.rossi95@libero.it	+39 06 555 2221
APP100017	prime	0.22	active	CUST1017	2023-06-30	ACC100017	2023-07-04	3750	1200	32	2023-08-04	2023-08-02	360	36	on-time	540	110	0	15.7	112 George St	Sydney	NSW	2000	Australia	1993-07-04	employed	48200	olivia.taylor93@bigpond.com	+61 2 5550 7789
APP100018	super-prime	0.07	closed	CUST1018	2022-03-12	ACC100018	2022-03-18	42000	0	0	2022-04-18	2022-04-15	1800	0	on-time	0	0	0	7.1	66 Dunsmuir St	Vancouver	BC	V6B 1Y1	Canada	1956-05-24	retired	0	william.miller56@shaw.ca	+1-604-555-1008
APP100019	prime	0.27	active	CUST1019	2023-11-20	ACC100019	2023-11-24	5500	1870	34	2023-12-24	2023-12-22	670	56.1	on-time	710	130	0	15.5	8 Princes St	Edinburgh	Scotland	EH2 2ER	UK	1990-02-09	employed	47800	fiona.macgregor90@icloud.com	+44 131 555 7491
APP100020	subprime	0.91	pending	CUST1020	2024-01-20	blank	blank	0	0	0	2024-02-20	blank	blank	0	missed	0	0	0	30.9	110 Avenida Chile	Bogotá	Cundinamarca	110311	Colombia	2001-11-01	student	0	juan.perez01@uniandes.edu.co	+57 1 555 0918

What the 700 rows show

from the 700-row sample

Subprime (pricing tier) stands out: mean risk_score is 0.89, against 0.25 for the rest.

0.28median risk_score
3payment statuses
4employment statuses
35address countries
89address cities
102address states

Mean risk_score by pricing_tier699 rows

risk_score699 rows, in bands of 0.1

Median 0.28, from 0.03 to 1.0.

account_status699 rows with a value · 1 left blank

active527
closed72
pending63
suspended37

30 columns by typefrom the column list below

string 14
integer 3
float 8
date 5

Columns

30 columns in three groups

blueprint · 30 columns
column	type	description	example
Text 14 columns
`application_id`	string	Unique identifier for each credit card applicationunique	APP100001
`customer_id`	string	Unique identifier for each customer	CUST1001
`account_id`	string	Unique identifier for each credit card accountunique · optional	ACC100001
`account_status`	string	Current status of the account (e.g., active, closed, suspended, pending)active · closed · suspended · pending	active
`payment_status`	string	Status of the most recent payment (e.g., on-time, late, missed)on-time · late · missed	on-time
`pricing_tier`	string	Risk-based pricing tier assigned to the account (e.g., prime, subprime, super-prime)prime · subprime · super-prime	prime
`address_street`	string	Customer's street address	431 Oak Ridge Ave
`address_city`	string	Customer's city	Cleveland
`address_state`	string	Customer's state or province	OH
`address_postal_code`	string	Customer's postal or ZIP code	44109
`address_country`	string	Customer's country	USA
`employment_status`	string	Customer's employment status (e.g., employed, unemployed, retired, student)employed · unemployed · retired · student	employed
`email`	string	Customer's email addressunique	sophia.chan84@mail.ca
`phone_number`	string	Customer's phone number	+1-216-555-0933
Numbers 11 columns
`credit_limit`	float	Maximum credit limit assigned to the account0 or more	7500
`current_balance`	float	Current outstanding balance on the account0 or more	2550
`utilization_rate`	float	Ratio of current balance to credit limit, expressed as a percentage (0-100)0 to 100	34
`last_payment_amount`	float	Amount paid in the last payment0 or more · optional	850
`minimum_payment_due`	float	Minimum payment required for the current billing cycle0 or more	127.5
`rewards_points_balance`	integer	Current balance of rewards points for the account0 or more · optional	1560
`rewards_points_earned`	integer	Total rewards points earned in the current billing cycle0 or more · optional	320
`rewards_points_redeemed`	integer	Total rewards points redeemed in the current billing cycle0 or more · optional	0
`risk_score`	float	Calculated risk score for the account based on payment history and utilization0 to 1	0.28
`annual_percentage_rate`	float	Annual percentage rate (APR) applied to the account0 to 100	15.4
`annual_income`	float	Customer's reported annual income0 or more	56500
Dates and times 5 columns
`application_date`	date	Date when the credit card application was submitted	2023-09-14
`account_open_date`	date	Date when the credit card account was openedoptional	2023-09-17
`payment_due_date`	date	Date when the next payment is due	2023-10-17
`last_payment_date`	date	Date when the last payment was madeoptional	2023-10-15
`date_of_birth`	date	Customer's date of birth	1990-07-22

Use it for

A finance dashboard
Risk_score by pricing_tier and a breakdown of account_status. Excel, Power BI or Tableau.
Why do the 163 subprime rows have a mean risk_score of 0.89?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applications700
APP1000010.28prime
APP1000020.21prime
APP1000030.08super-pr…
A software demo
Believable applications with customer_id, application_date and account_id to fill a screen in front of a buyer.

This dataset700 rows30 columns

Yours1,000 rows30 columnsaddress_street: UK only

blueprint · credit-card-risk-assessment

Behind this dataset

Same schema. As many rows as you need.

These 700 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

Application scorecard determines approval
Initial credit limit based on credit score and income
APR tiered by creditworthiness
Credit line increase criteria
Utilization rate monitored: ideal under 30%
Minimum payment: greater of $25 or 1% + interest
Overlimit fees restricted by CARD Act
Behavioral scoring for existing accounts

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: credit-card-risk-assessment

What should your data show?

No signup. No card.

Sample rows

What the 700 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?