Credit Card Usage Patterns

This dataset provides detailed, anonymized credit card transaction records, including customer demographics, merchant information, and transaction characteristics. It is designed for in-depth analysis of customer behavior, marketing campaign effectiveness, and fraud detection, making it valuable for financial institutions and data scientists seeking actionable insights.

  • opened 6 times
  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Customer segmentation and behavioral analysis

Sample rows

preview · 8 of 200 rows · all 20 columns
transaction_idstringtransaction_typestringtransaction_amountfloatis_fraudbooleanlocation_statestringcustomer_idstringtransaction_datetimedatetimemerchant_idstringmerchant_namestringmerchant_categorystringcurrencystringcard_typestringis_onlinebooleandevice_typestringlocation_citystringlocation_postal_codestringlocation_countrystringcustomer_age_groupstringcustomer_genderstringmarketing_campaign_idstring
TXN_01A2B3C4D5purchase42.75falseILCUST0210XK2024-05-17T13:47:22ZMCH_1932RTFreshMartgroceryUSDVisafalsePOSChicago60611US26-35femaleMC_SPRING24
TXN_0G3T5F8R9Qpurchase1179.99falseCACUST9831ZX2024-06-03T10:18:05ZMCH_2918LMTech WorldelectronicsUSDMasterCardtruedesktopSan Francisco94105US36-45maleMC_SUMMER24
TXN_1Q4W6E7R8Tpurchase68.4falseTXCUST0022AB2024-05-21T16:03:14ZMCH_9934STCafe MocharestaurantUSDVisafalsePOSAustin78701US46-55femaleMC_SPRING24
TXN_2A5S4D6F3Gpurchase219.99falseNYCUST2345CD2024-05-30T19:22:51ZMCH_8762JKStyle HubclothingUSDMasterCardtruemobileNew York10001US26-35maleMC_SPRING24
TXN_3Z6X9C7V2Bpurchase54.8falseTXCUST5241EF2024-06-09T08:56:44ZMCH_6541GHFuelZonefuelUSDVisafalsePOSDallas75201US36-45femaleblank
TXN_4H8J5K2L1Npurchase1450falseCOCUST7770GH2024-04-22T17:34:12ZMCH_4729POGoTraveltravelUSDVisatruedesktopDenver80202US56-65maleblank
TXN_5Q9W8E7R6Tpurchase36.19falseILCUST0210XK2024-05-27T15:09:27ZMCH_1932RTFreshMartgroceryUSDVisafalsePOSChicago60611US26-35femaleMC_SPRING24
TXN_6E2T8G5V1Bpurchase77.5falseCACUST6332HI2024-06-12T14:22:08ZMCH_1881LKMovieTimeentertainmentUSDAmextruetabletLos Angeles90017US36-45maleMC_MOVIENIGHT24

What the 200 rows show

from the 200-row sample

Cash_advance (transaction type) stands out: 15 of its 16 rows have is_fraud = true, against 31 of 184 for the rest.

  • 23%is_fraud = true
  • 87.0median transaction_amount
  • 4customer genders
  • 5card types
  • 5device types
  • 6currencies
Is fraud rate by transaction_typeis_fraud = true
0%50%100%15%purchase23 of 15094%cash_adv…15 of 1650%payment3 of 618%fee5 of 28
transaction_amount200 rows, in bands of 100k
01002001910000000090500k1Mtransaction_amount →

Median 87.0, from 0.01 to 1,000,000.

location_state167 rows with a value · 33 left blank
  1. CA39
  2. TX34
  3. IL18
  4. MA13
  5. FL11
  6. NY10
  7. WA9
  8. AZ8
  9. PA7
  10. NC5
20 columns by typefrom the column list below
  • string 16
  • float 1
  • datetime 1
  • boolean 2

Columns

20 columns in four groups
blueprint · 20 columns
columntypedescriptionexample
Text 16 columns
transaction_idstringUnique identifier for each credit card transactionuniqueTXN_01A2B3C4D5
customer_idstringAnonymous unique identifier for the customer making the transactionCUST0210XK
merchant_idstringUnique identifier for the merchant where the transaction took placeMCH_1932RT
merchant_namestringName of the merchant where the transaction occurredoptionalFreshMart
merchant_categorystringCategory of the merchant (e.g., grocery, electronics, restaurant)8 valuesgrocery
currencystringISO 4217 currency code for the transaction (e.g., USD, EUR)6 currenciesUSD
transaction_typestringType of transaction (e.g., purchase, refund, cash_advance)6 valuespurchase
card_typestringType of credit card used (e.g., Visa, MasterCard, Amex)Visa · MasterCard · Amex · Discover · OtherVisa
device_typestringType of device used for the transaction (e.g., mobile, desktop, POS terminal)mobile · desktop · POS · tablet · other · optionalPOS
location_citystringCity where the transaction occurredoptionalChicago
location_statestringState or province where the transaction occurredoptionalIL
location_postal_codestringPostal code of the transaction locationoptional60611
location_countrystringCountry where the transaction occurred (ISO 3166-1 alpha-2 code)8 countriesUS
customer_age_groupstringAge group of the customer (e.g., 18-25, 26-35, etc.)6 values · optional26-35
customer_genderstringGender of the customer (if available)male · female · other · unknown · optionalfemale
marketing_campaign_idstringIdentifier for the marketing campaign associated with the transaction, if anyoptionalMC_SPRING24
Numbers 1 column
transaction_amountfloatTotal amount of the transaction in local currency0.01 or more42.75
Dates and times 1 column
transaction_datetimedatetimeDate and time when the transaction occurred2024-05-17T13:47:22Z
True or false 2 columns
is_onlinebooleanIndicates if the transaction was made online (true) or in-person (false)false
is_fraudbooleanFlag indicating if the transaction was identified as fraudulentoptionalfalse

Use it for

  • is fraud23%46 of 200 rowsmean transaction amou…967.8purc…63.7kcash…500kpaym…178.6kfee

    A finance dashboard

    The is_fraud rate, transaction_amount by transaction_type and a breakdown of location_state. Excel, Power BI or Tableau.

  • Why do 46 of 200 rows have is_fraud = true?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable transactions with customer_id, transaction_datetime and merchant_id to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 20 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows20 columns
Yours1,000 rows20 columnslocation_city: UK only

blueprint · credit-card-usage-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 20 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
  • Transaction amounts consistent with industry norms
  • Cardholder demographics included
  • Merchant types classified
  • Usage frequency tracked monthly
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-usage-patterns

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