Finance
18 columns
200 rows
8 formats

Credit Card Limit Optimization

This dataset provides detailed records of credit card accounts, including current and optimal credit limits, risk and utilization metrics, payment history, and historical limit adjustments. Designed for credit risk modeling and customer optimization, it supports advanced analytics for limit management, customer segmentation, and predictive modeling in financial services.

last updated 29 Jul 2025
by GoMask

The brief that made it

Credit limit optimization and risk management modeling

Sample rows

preview · 8 of 200 rows · all 18 columns

account_idstring	risk_segmentstring	utilization_ratefloat	delinquency_flagboolean	limit_adjustment_reasonstring	customer_idstring	account_open_datedate	current_credit_limitfloat	optimal_credit_limitfloat	last_limit_adjustment_datedate	last_limit_adjustment_amountfloat	account_statusstring	credit_scoreinteger	payment_history_statusstring	annual_incomefloat	employment_statusstring	account_typestring	regionstring
ACCT0001	low	0.08	false	automatic increase	CUST0001	2017-04-22	85000	90000	2022-04-15	5000	active	798	on_time	250000	employed	business	New York City
ACCT0002	medium	0.22	false	blank	CUST0002	2021-11-05	5800	6700	blank	blank	active	691	on_time	67000	employed	personal	Los Angeles
ACCT0003	high	0.72	true	risk review	CUST0003	2019-08-30	1750	1500	2020-01-12	-250	active	597	late	11000	student	secured	Unknown
ACCT0004	low	0.03	false	customer request	CUST0004	2018-03-14	4000000	4200000	2023-02-21	200000	active	891	on_time	8000000	self_employed	business	International
ACCT0005	medium	0.19	false	blank	CUST0005	2020-07-12	3000	3300	blank	blank	active	712	on_time	48000	employed	personal	Chicago
ACCT0006	high	0.97	true	blank	CUST0006	2022-01-03	1200	1000	blank	blank	delinquent	412	missed	0	unemployed	secured	Northeast
ACCT0007	low	0.13	false	automatic increase	CUST0007	2013-10-06	14500	17000	2022-10-15	2500	active	765	on_time	116000	employed	personal	Pacific Northwest
ACCT0008	low	0.09	false	customer request	CUST0008	2015-02-17	32000	39000	2021-05-21	7000	active	776	on_time	140000	self_employed	business	San Francisco Bay
ACCT0009	high	0.91	true	blank	CUST0009	2022-09-28	950	900	blank	blank	suspended	335	missed	0	unknown	secured	Unknown
ACCT0010	medium	0.23	false	automatic increase	CUST0010	2020-03-08	8700	10000	2022-07-01	1300	active	702	on_time	76000	employed	personal	Dallas
ACCT0011	low	0.06	false	automatic increase	CUST0011	2018-06-19	120000	128000	2021-09-15	8000	active	819	on_time	210000	employed	business	Houston
ACCT0012	low	0.15	false	customer request	CUST0012	2016-12-23	4000	4800	2022-06-30	800	active	781	on_time	95000	employed	personal	Miami
ACCT0013	low	0.02	false	automatic increase	CUST0013	2014-02-11	820000	900000	2021-01-27	80000	active	872	on_time	1250000	self_employed	business	Offshore
ACCT0014	high	0.98	true	blank	CUST0014	2017-10-01	400	350	blank	blank	delinquent	350	missed	0	student	secured	Southwest
ACCT0015	low	0.12	false	automatic increase	CUST0015	2019-05-16	17500	21000	2022-02-23	3500	active	789	on_time	119000	employed	personal	Atlanta
ACCT0016	medium	0.18	false	customer request	CUST0016	2019-09-19	6400	6500	2021-11-13	100	active	742	on_time	57000	employed	personal	Mid-Atlantic
ACCT0017	low	0.04	false	automatic increase	CUST0017	2015-04-22	500000	560000	2020-08-16	60000	active	865	on_time	740000	self_employed	business	International
ACCT0018	low	0.13	false	customer request	CUST0018	2018-12-05	9500	11500	2022-03-29	2000	active	788	on_time	100000	employed	personal	Seattle
ACCT0019	high	0.81	true	risk review	CUST0019	2017-03-23	1950	1800	2021-10-10	-150	active	588	late	8000	student	secured	Midwest
ACCT0020	low	0.1	false	customer request	CUST0020	2013-02-15	27500	28000	2022-02-01	500	active	799	on_time	134000	employed	business	San Diego

What the 200 rows show

from the 200-row sample

High (risk segment) stands out: 56 of its 56 rows have delinquency_flag = true, against 1 of 144 for the rest.

28%delinquency_flag = true
0.18median utilization_rate
3payment history statuses
4account statuses
4account types
6employment statuses

Delinquency flag rate by risk_segmentdelinquency_flag = true

High (risk segment)'s mean utilization_rate is 0.88, against 0.08 for low and 0.19 for medium.

utilization_rate200 rows, in bands of 0.1

Median 0.18, from 0.01 to 0.99.

limit_adjustment_reason163 rows with a value · 37 left blank

customer request58
automatic increase45
risk review36
periodic review7
annual review7
reviewed income5
credit builder review5

18 columns by typefrom the column list below

string 9
integer 1
float 5
date 2
boolean 1

Columns

18 columns in four groups

blueprint · 18 columns
column	type	description	example
Text 9 columns
`account_id`	string	Unique identifier for the credit card accountunique	ACCT0001
`customer_id`	string	Unique identifier for the customer who owns the account	CUST0001
`limit_adjustment_reason`	string	Reason for the last credit limit adjustment (e.g., customer request, risk review, automatic increase)7 reasons · optional	automatic increase
`account_status`	string	Current status of the credit card accountactive · closed · suspended · delinquent	active
`payment_history_status`	string	Summary of recent payment history (e.g., on_time, late, missed)on_time · late · missed	on_time
`risk_segment`	string	Risk category assigned to the account (e.g., low, medium, high)low · medium · high	low
`employment_status`	string	Employment status of the account holder6 values · optional	employed
`account_type`	string	Type of credit card account (e.g., personal, business, secured, unsecured)personal · business · secured · unsecured	business
`region`	string	Geographic region or market of the account holderoptional	New York City
Numbers 6 columns
`current_credit_limit`	float	Current approved credit limit for the account0 or more	85000
`optimal_credit_limit`	float	Recommended optimal credit limit for the account based on risk and usage modeling0 or more	90000
`last_limit_adjustment_amount`	float	Amount by which the credit limit was last adjusted (positive for increase, negative for decrease)optional	5000
`credit_score`	integer	Most recent credit score of the account holder300 to 900	798
`utilization_rate`	float	Recent average utilization rate (ratio of balance to credit limit, 0-1)0 to 1	0.08
`annual_income`	float	Reported annual income of the account holder0 or more · optional	250000
Dates and times 2 columns
`account_open_date`	date	Date when the credit card account was opened	2017-04-22
`last_limit_adjustment_date`	date	Date of the most recent credit limit adjustmentoptional	2022-04-15
True or false 1 column
`delinquency_flag`	boolean	Indicates if the account is currently delinquent	false

Use it for

A finance dashboard
The delinquency_flag rate, utilization_rate by risk_segment and a breakdown of limit_adjustment_reason. Excel, Power BI or Tableau.
Why do 57 of 200 rows have delinquency_flag = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Accounts200
ACCT00010.08low
ACCT00030.72high
ACCT00060.97high
A software demo
Believable accounts with customer_id, account_open_date and current_credit_limit to fill a screen in front of a buyer.

This dataset200 rows18 columns

Yours1,000 rows18 columnsregion: UK only

blueprint · credit-card-limit-optimization

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 18 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

Accounts have assigned credit scores
Limit changes logged with rationale
Delinquency and usage tracked monthly
Recommendations labeled for risk level

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-card-limit-optimization

What should your data show?

No signup. No card.

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?