Finance
22 columns
200 rows
8 formats

Finance Credit Card Spend Analysis

This dataset provides granular credit card transaction records, including customer demographics, card details, merchant information, and transaction metadata. It is ideal for banks and fintechs seeking to analyze spending patterns, segment customers, and model risk, enabling data-driven product design and market research.

last updated 20 Aug 2025
by GoMask

The brief that made it

Credit card spend pattern analysis for product development

Sample rows

preview · 8 of 200 rows · all 22 columns

transaction_idstring	customer_income_bracketstring	customer_ageinteger	is_internationalboolean	merchant_categorystring	customer_idstring	card_idstring	transaction_datetimedatetime	transaction_amountfloat	merchant_namestring	merchant_citystring	merchant_statestring	merchant_countrystring	transaction_typestring	card_typestring	customer_genderstring	customer_segmentstring	transaction_currencystring	card_issuer_bankstring	transaction_statusstring	customer_postal_codestring	customer_countrystring
TXN000001	low	22	false	groceries	CUST0001	CARD0001	2024-06-01T09:42:18	120.5	FreshMarket	New York	NY	USA	purchase	Visa	female	student	USD	Chase	completed	10013	USA
TXN000002	medium	27	true	dining	CUST0002	CARD0002	2024-06-01T16:37:04	350	UrbanBistro	London	LDN	GBR	purchase	MasterCard	male	standard	GBP	Barclays	completed	W1D4FA	USA
TXN000003	medium	33	false	retail	CUST0003	CARD0003	2024-06-02T10:10:29	45.75	QuickMart	Chicago	IL	USA	purchase	Visa	female	standard	USD	Bank of America	completed	60611	USA
TXN000004	medium	33	false	retail	CUST0004	CARD0004	2024-06-02T18:25:47	-65.3	QuickMart	Chicago	IL	USA	refund	Visa	female	standard	USD	Bank of America	completed	60611	USA
TXN000005	high	51	true	travel	CUST0005	CARD0005	2024-06-03T11:55:03	2500	FlyHighAir	Dubai	DU	ARE	purchase	Amex	male	premium	AED	EmiratesNBD	completed	10123	USA
TXN000006	medium	40	true	groceries	CUST0006	CARD0006	2024-06-03T12:44:21	510.2	GreenGrocer	Berlin	BE	DEU	purchase	MasterCard	female	standard	EUR	DeutscheBank	completed	10178	USA
TXN000007	medium	26	true	dining	CUST0007	CARD0007	2024-06-04T09:16:22	86	CafeParis	Paris	IDF	FRA	purchase	Visa	other	student	EUR	BNPParibas	completed	75006	USA
TXN000008	low	21	false	retail	CUST0008	CARD0008	2024-06-04T13:27:12	44.99	UrbanThreads	Toronto	ON	CAN	purchase	Visa	female	student	CAD	RoyalBank	completed	M5V3C6	CAN
TXN000009	medium	24	true	entertainment	CUST0009	CARD0009	2024-06-04T19:55:45	320	CinemaWorld	London	LDN	GBR	purchase	MasterCard	male	student	GBP	HSBC	completed	N1C4AH	USA
TXN000010	medium	25	true	groceries	CUST0010	CARD0010	2024-06-05T08:05:13	89.99	DailyFresh	Melbourne	VIC	AUS	purchase	Visa	female	student	AUD	Westpac	completed	3006	USA
TXN000011	medium	30	false	retail	CUST0011	CARD0011	2024-06-05T12:15:54	70.25	StyleDepot	Mumbai	MH	IND	purchase	MasterCard	male	standard	INR	HDFC	completed	400021	IND
TXN000012	high	59	true	jewelry	CUST0002	CARD0002	2024-06-05T18:22:38	6500	GenevaJewels	Geneva	GE	CHE	purchase	Amex	female	premium	CHF	UBS	completed	1201	USA
TXN000013	low	18	false	groceries	CUST0012	CARD0012	2024-06-06T07:41:30	35	FreshMarket	New York	NY	USA	purchase	Visa	female	student	USD	Chase	completed	10012	USA
TXN000014	high	74	true	automotive	CUST0013	CARD0013	2024-06-06T13:19:43	12000	AutoWorld	Tokyo	13	JPN	purchase	Visa	male	premium	JPY	MUFG	completed	160-0022	USA
TXN000015	medium	23	true	dining	CUST0014	CARD0014	2024-06-07T09:03:17	87.9	UrbanBistro	London	LDN	GBR	purchase	MasterCard	male	student	GBP	Barclays	completed	SW1A1AA	USA
TXN000016	medium	37	true	electronics	CUST0015	CARD0015	2024-06-07T14:28:19	375	GadgetStore	Berlin	BE	DEU	purchase	Visa	female	standard	EUR	DeutscheBank	completed	10999	USA
TXN000017	medium	44	true	groceries	CUST0016	CARD0016	2024-06-08T10:54:22	125	GreenGrocer	Berlin	BE	DEU	purchase	MasterCard	male	standard	EUR	Commerzbank	completed	10557	USA
TXN000018	medium	33	false	retail	CUST0004	CARD0004	2024-06-08T15:32:09	119	QuickMart	Chicago	IL	USA	purchase	Visa	female	standard	USD	Bank of America	completed	60611	USA
TXN000019	high	65	true	healthcare	CUST0017	CARD0017	2024-06-09T11:43:06	900	MedCare	Paris	IDF	FRA	purchase	Amex	female	premium	EUR	BNPParibas	completed	75015	USA
TXN000020	medium	29	true	dining	CUST0018	CARD0018	2024-06-09T17:10:56	180.1	CafeParis	Paris	IDF	FRA	purchase	Visa	male	standard	EUR	BNPParibas	completed	75007	USA

What the 200 rows show

from the 200-row sample

High (customer income bracket) stands out: mean customer_age is 68.0, against 31.7 for the rest.

57%is_international = true
36median customer_age
3card types
3customer segments
3transaction statuses
4transaction types

Mean customer_age by customer_income_bracket200 rows

customer_age200 rows, in bands of 20

Median 36, from 18 to 120.

merchant_category198 rows with a value · 2 left blank

groceries48
retail36
dining21
travel19
entertainment14
healthcare14
electronics13
automotive11
jewelry10
utilities10

22 columns by typefrom the column list below

string 18
integer 1
float 1
datetime 1
boolean 1

Columns

22 columns in four groups

blueprint · 22 columns
column	type	description	example
Text 18 columns
`transaction_id`	string	Unique identifier for each credit card transactionunique	TXN000001
`customer_id`	string	Unique identifier for the customer making the transaction	CUST0001
`card_id`	string	Unique identifier for the credit card used	CARD0001
`merchant_category`	string	Category of the merchant (e.g., groceries, travel, dining)12 categories	groceries
`merchant_name`	string	Name of the merchant where the transaction occurredoptional	FreshMarket
`merchant_city`	string	City where the merchant is locatedoptional	New York
`merchant_state`	string	State or region where the merchant is locatedoptional	NY
`merchant_country`	string	Country where the merchant is located11 countries · optional	USA
`transaction_type`	string	Type of transaction (e.g., purchase, cash advance, refund)purchase · cash_advance · refund · payment	purchase
`card_type`	string	Type of credit card used (e.g., Visa, MasterCard, Amex)3 types	Visa
`customer_gender`	string	Gender of the customermale · female · other · prefer_not_to_say · optional	female
`customer_income_bracket`	string	Income bracket of the customer (e.g., low, medium, high)low · medium · high · unknown · optional	low
`customer_segment`	string	Segment classification of the customer (e.g., premium, standard, student)3 segments · optional	student
`transaction_currency`	string	Currency code used in the transaction (e.g., USD, EUR)9 currencies	USD
`card_issuer_bank`	string	Name of the bank or institution that issued the credit cardoptional	Chase
`transaction_status`	string	Status of the transaction (e.g., completed, pending, declined)completed · pending · declined · reversed	completed
`customer_postal_code`	string	Postal code of the customer's primary addressoptional	10013
`customer_country`	string	Country of the customer's primary address7 countries · optional	USA
Numbers 2 columns
`transaction_amount`	float	Total amount spent in the transaction0.01 or more	120.5
`customer_age`	integer	Age of the customer at the time of transaction18 to 120 · optional	22
Dates and times 1 column
`transaction_datetime`	datetime	Date and time when the transaction occurred	2024-06-01T09:42:18
True or false 1 column
`is_international`	boolean	Indicates if the transaction was made outside the cardholder's home countryoptional	false

Use it for

A finance dashboard
The is_international rate, customer_age by customer_income_bracket and a breakdown of merchant_category. Excel, Power BI or Tableau.
Why do the 48 high rows have a mean customer_age of 68.0?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Transactions200
TXN00000122low
TXN00000227medium
TXN00000551high
A software demo
Believable transactions with customer_id, card_id and transaction_datetime to fill a screen in front of a buyer.

This dataset200 rows22 columns

Yours1,000 rows22 columnsmerchant_city: UK only

blueprint · finance-credit-card-spend-analysis

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 22 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

Classify spend by merchant category
Track monthly spend per customer
Identify high-value transaction clusters
Flag unusual spend spikes

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-credit-card-spend-analysis

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?