Card Fraud Patterns

This dataset provides detailed records of credit and debit card fraud incidents, including transaction details, fraud types, merchant and cardholder information, detection methods, and case resolution status. It enables comprehensive analysis of fraud patterns, financial losses, and recovery efforts, supporting risk management and regulatory compliance in the financial sector.

  • opened 9 times
  • last updated 1 Nov 2025
  • by GoMask
The brief that made it

Fraud pattern analysis and risk assessment

Sample rows

preview · 8 of 600 rows · all 25 columns
fraud_idstringfraud_typestringloss_amountfloatis_internationalbooleanmerchant_countrystringcard_number_maskedstringcard_typestringissuer_bankstringfraud_datetimedatetimetransaction_amountfloatcurrencystringmerchant_namestringmerchant_categorystringmerchant_citystringmerchant_statestringmerchant_postal_codestringdetection_methodstringcompromised_atm_idstringcompromised_terminal_idstringcardholder_zipstringcardholder_countrystringreported_to_authoritiesbooleanresolution_statusstringrecovery_amountfloatnotesstring
FRD000001skimming65.21falseUSA4926creditCitibank2023-12-04T09:46:33Z85.21USDblankATMSan FranciscoCA94108customer reportATM401812blank10011USAtrueunder investigation20ATM skimming detected during week after Thanksgiving.
FRD000002skimming0falseUSA7318debitWells Fargo2023-11-28T17:51:45Z205.9USDblankATMAustinTX78705automated systemATM578210blank78701USAfalseresolved205.9Automated detection of skimming at campus ATM, full recovery.
FRD000003lost/stolen0falseUSA1684prepaidChime2024-01-17T12:23:09Z48.75USD7-ElevenretailChicagoIL60616customer reportblankblank60640USAfalseresolved48.75Cardholder reported card lost, transaction reversed.
FRD000004counterfeit0falseUSA5291debitBank of America2023-10-29T21:15:19Z0USDTargetretailSan DiegoCA92110automated systemblankblank92122USAtruewritten off0Counterfeit card attempt detected; no loss incurred.
FRD000005merchant compromise17850trueGBR8452creditHSBC2023-09-15T14:05:31Z21850GBPHarrodsretailLondonENGSW1X 7XLautomated systemblankTERM00834W1A 1AAUSAtrueunder investigation4000Large merchant compromise, POS terminal breach under investigation.
FRD000006skimming325.6falseCAN3076debitTD Bank2024-02-07T08:44:56Z325.6CADblankATMTorontoONM5V 2T6manual reviewATM215743blankM4B 1B3CANfalseopen0Manual review of ATM logs revealed skimming incident.
FRD000007CNP0trueDEU1648creditSantander2024-01-25T19:16:03Z799.99EURAmazononlineBerlinBE10117automated systemblankblankSW1A 2AAGBRtrueresolved799.99CNP fraud flagged on Amazon order, full recovery issued.
FRD000008lost/stolen0falseFRA3420prepaidN262023-11-08T16:32:14Z72.6EURCarrefourretailParisIDF75008customer reportblankblank75016FRAfalseresolved72.6Card reported stolen, swift recovery of funds.

What the 600 rows show

from the 600-row sample

Merchant compromise (fraud type) stands out: mean loss_amount is 14,780, against 355.2 for the rest.

  • 25%is_international = true
  • 194.5median loss_amount
  • 3card types
  • 4merchant categories
  • 4detection methods
  • 4resolution statuses
Mean loss_amount by fraud_type600 rows
010k20k352.9skimmi…143 rows82.8CNP119 rows0.0lost/s…108 rows990.0counte…112 rows14,780mercha…118 rows
loss_amount600 rows, in bands of 5,000
0245490488173537185015k30kloss_amount →

Median 194.5, from 0.0 to 28,700.

merchant_country600 rows · top 10 of 24 values
  1. USA206
  2. GBR76
  3. CAN52
  4. AUS43
  5. DEU35
  6. FRA30
  7. ESP28
  8. NLD27
  9. CHE16
  10. ITA13
25 columns by typefrom the column list below
  • string 19
  • float 3
  • datetime 1
  • boolean 2

Columns

25 columns in four groups
blueprint · 25 columns
columntypedescriptionexample
Text 19 columns
fraud_idstringUnique identifier for each fraud incidentuniqueFRD000001
card_number_maskedstringMasked card number involved in the fraud (e.g., last 4 digits)4926
card_typestringType of card involved (e.g., credit, debit, prepaid)credit · debit · prepaidcredit
issuer_bankstringName of the bank that issued the cardCitibank
fraud_typestringType of fraud detected (e.g., skimming, CNP, lost/stolen, counterfeit, merchant compromise)5 valuesskimming
currencystringCurrency code for the transaction (ISO 4217 format)USD
merchant_namestringName of the merchant where the fraud occurred (if applicable)optional7-Eleven
merchant_categorystringCategory of the merchant (e.g., retail, online, ATM, restaurant)4 categories · optionalATM
merchant_citystringCity where the merchant is locatedoptionalSan Francisco
merchant_statestringState or region of the merchant locationoptionalCA
merchant_postal_codestringPostal code of the merchant locationoptional94108
merchant_countrystringCountry of the merchant locationoptionalUSA
detection_methodstringMethod used to detect the fraud (e.g., manual review, automated system, customer report)4 valuescustomer report
compromised_atm_idstringIdentifier for the ATM involved in skimming or compromise (if applicable)optionalATM401812
compromised_terminal_idstringIdentifier for the POS terminal involved in compromise (if applicable)optionalTERM00834
cardholder_zipstringPostal code of the cardholder's billing addressoptional10011
cardholder_countrystringCountry of the cardholder's billing addressoptionalUSA
resolution_statusstringStatus of the fraud case (e.g., open, resolved, under investigation, written off)open · resolved · under investigation · written offunder investigation
notesstringAdditional notes or comments about the fraud incidentoptionalATM skimming detected dur…
Numbers 3 columns
transaction_amountfloatAmount involved in the fraudulent transaction0 or more85.21
loss_amountfloatConfirmed financial loss due to the fraud (may be less than transaction amount if recovered)0 or more · optional65.21
recovery_amountfloatAmount recovered from the fraudulent transaction0 or more · optional20
Dates and times 1 column
fraud_datetimedatetimeDate and time when the fraudulent activity occurred or was detected2023-12-04T09:46:33Z
True or false 2 columns
is_internationalbooleanIndicates if the transaction was internationalfalse
reported_to_authoritiesbooleanIndicates if the fraud was reported to law enforcement or regulatory authoritiesoptionaltrue

Use it for

  • is internation…25%150 of 600 rowsmean loss amount by f…352.9skim…82.8CNP0.0lost…990.0coun…

    A finance dashboard

    The is_international rate, loss_amount by fraud_type and a breakdown of merchant_country. Excel, Power BI or Tableau.

  • Why do the 118 merchant compromise rows have a mean loss_amount of 14,780?

    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 frauds with card_number_masked, card_type and issuer_bank to fill a screen in front of a buyer.

Not quite right?

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This dataset600 rows25 columns
Yours10,000 rows25 columnsmerchant_city: UK only

blueprint · card-fraud-patterns-2

Behind this dataset

Same schema. As many rows as you need.

These 600 rows came out of a blueprint — 25 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
  • Fraud type: lost/stolen, counterfeit, CNP, skimming, shimming
  • Card number tokenized for security
  • Merchant Category Code analysis
  • Geographic location anomalies
  • Time-since-last-transaction analysis
  • EMV chip vs magstripe indicator
  • Card present vs card not present
  • Merchant compromise patterns
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
card-fraud-patterns-2

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