Card Fraud Patterns

This dataset provides a detailed record of credit and debit card fraud incidents, capturing transaction specifics, fraud types, customer demographics, merchant details, and investigation outcomes. It enables comprehensive analysis of fraud patterns, geographic hotspots, and merchant vulnerabilities, supporting risk management and fraud prevention strategies in the financial sector.

  • last updated 29 Oct 2025
  • by GoMask
The brief that made it

Fraud pattern analysis and hotspot identification

Sample rows

preview · 8 of 600 rows · all 31 columns
fraud_idstringfraud_typestringtransaction_amountfloatis_card_presentbooleanmerchant_categorystringcard_number_hashstringcard_typestringissuer_bankstringtransaction_idstringtransaction_datetimedatetimecurrencystringmerchant_namestringmerchant_citystringmerchant_statestringmerchant_countrystringdetection_methodstringreported_datetimedatetimeresolution_statusstringcustomer_id_hashstringcustomer_ageintegercustomer_genderstringcustomer_citystringcustomer_statestringcustomer_countrystringcompromise_location_typestringcompromise_citystringcompromise_statestringcompromise_countrystringis_counterfeit_detectedbooleanis_merchant_compromisedbooleanadditional_notesstring
FRD-00001CNP328.45falseOnline RetailCARD-9A7F2C3B8D4E1F6AcreditWells Fargo BankTXN-10012023-02-12T14:23:17ZUSDAmazon OnlineSeattleWAUSAautomated system2023-02-12T14:58:32Zunder investigationCUST-8EF3B7D9A1C5E4F229femaleLos AngelesCAUSAonlineLos AngelesCAUSAfalsefalseCNP fraud detected via automated system. Customer confirmed transaction as unauthorized.
FRD-00002skimming120.89trueFuel StationsCARD-2F1B4E7C9A8D5E3FdebitChase BankTXN-10022023-03-01T09:12:44ZUSDQuickStop FuelChicagoILUSAcustomer report2023-03-02T13:22:09ZconfirmedCUST-4C7D9E2A1B5F8E6C34maleChicagoILUSAATMChicagoILUSAfalsefalseCustomer noticed unauthorized ATM withdrawals. Skimming devices found on ATM.
FRD-00003lost/stolen580trueElectronicsCARD-E4F3B2A8D1C7E9F0creditBank of AmericaTXN-10032023-01-18T19:31:26ZUSDBest BuyDallasTXUSAcustomer report2023-01-19T14:05:22ZresolvedCUST-6D7A1E2F4C9B8E3F56femaleDallasTXUSAPOS terminalDallasTXUSAfalsefalseCard reported lost, unauthorized POS transaction detected and resolved.
FRD-00004counterfeit4500.75trueJewelryCARD-1F9E7C3B2A4D8E6FcreditCiti BankTXN-10042023-05-10T18:47:09ZUSDDiamond JewelersLas VegasNVUSAmanual review2023-05-12T10:15:42Zunder investigationCUST-8B5C2E7F1A4D6E9F41maleLas VegasNVUSAmerchantLas VegasNVUSAtruefalseHigh-value purchase flagged. Counterfeit card confirmed after manual review.
FRD-00005merchant compromise230trueSupermarketsCARD-8D5E2C1B7A9F4E3CdebitHSBCTXN-10052023-04-03T15:25:32ZGBPTesco SupermarketLondonENGGBRautomated system2023-04-03T16:01:12ZconfirmedCUST-2A7E9D5C1F4B8E3F62femaleLondonENGGBRmerchantLondonENGGBRfalsetrueMerchant compromise detected. Multiple cards affected at Tesco Supermarket.
FRD-00006lost/stolen65.42trueSupermarketsCARD-7F4B8C2D1A9E6C3BprepaidRevolutTXN-10062023-06-30T21:15:48ZEURCarrefourParisIDFFRAcustomer report2023-07-01T09:03:16Zunder investigationCUST-4E9A2D7F1B8C5E3F19maleParisIDFFRAPOS terminalParisIDFFRAfalsefalsePrepaid card lost and used at Paris supermarket. Investigation ongoing.
FRD-00007skimming89.99trueATMsCARD-5A1C9B7E2F8D3E4CdebitUBSTXN-10072023-02-15T07:21:05ZCHFMigros ATMZurichZHCHEmanual review2023-02-17T11:43:29ZconfirmedCUST-3E2F7A9C1B5D8E4F67femaleBernBECHEATMZurichZHCHEfalsefalseSkimming detected at Zurich ATM. Confirmed after manual review.
FRD-00008CNP2120.65falseElectronicsCARD-4C8B2A1D7E3F9E6CcreditCapital OneTXN-10082023-07-17T02:15:38ZUSDApple Store OnlineCupertinoCAUSAautomated system2023-07-17T02:16:51Zunder investigationCUST-9A1D2F7B4C8E5F3C31maleSan JoseCAUSAonlineSan JoseCAUSAfalsefalseLarge online purchase flagged as CNP fraud. Automated detection.

What the 600 rows show

from the 600-row sample

Counterfeit (fraud type) stands out: mean transaction_amount is 22,984, against 499.9 for the rest.

  • 81%is_card_present = true
  • 193.4median transaction_amount
  • 3card types
  • 3detection methods
  • 3resolution statuses
  • 4customer genders
Mean transaction_amount by fraud_type600 rows
020k40k77.8skimmi…158 rows755.4CNP115 rows71.4lost/s…129 rows22,984counte…96 rows1,407mercha…102 rows
transaction_amount600 rows, in bands of 5,000
026553052437131111410710025k50ktransaction_amount →

Median 193.4, from 0.01 to 50,000.

merchant_category595 rows with a value · 5 left blank
  1. Supermarkets111
  2. Online Retail65
  3. Jewelry57
  4. ATMs53
  5. Electronics47
  6. Fuel Stations45
  7. Shopping Centers45
  8. ATM31
  9. Casinos24
  10. Supermarket23
31 columns by typefrom the column list below
  • string 24
  • integer 1
  • float 1
  • datetime 2
  • boolean 3

Columns

31 columns in four groups
blueprint · 31 columns
columntypedescriptionexample
Text 24 columns
fraud_idstringUnique identifier for each fraud incidentuniqueFRD-00001
card_number_hashstringHashed representation of the card number involved in the fraud (for privacy)CARD-9A7F2C3B8D4E1F6A
card_typestringType of card involved (e.g., credit, debit, prepaid)credit · debit · prepaidcredit
issuer_bankstringName of the bank that issued the cardWells Fargo Bank
fraud_typestringType of fraud detected (e.g., skimming, CNP, lost/stolen, counterfeit, merchant compromise)5 valuesCNP
transaction_idstringUnique identifier for the fraudulent transactionTXN-1001
currencystringCurrency code for the transaction amount (ISO 4217)12 currenciesUSD
merchant_namestringName of the merchant where the fraud occurredoptionalAmazon Online
merchant_categorystringMerchant category code or descriptionoptionalOnline Retail
merchant_citystringCity where the merchant is locatedoptionalSeattle
merchant_statestringState or province where the merchant is locatedoptionalWA
merchant_countrystringCountry where the merchant is locatedoptionalUSA
detection_methodstringMethod used to detect the fraud (e.g., manual review, automated system, customer report)manual review · automated system · customer reportautomated system
resolution_statusstringCurrent status of the fraud case (e.g., under investigation, confirmed, resolved, false positive)4 valuesunder investigation
customer_id_hashstringHashed identifier for the customer whose card was compromisedCUST-8EF3B7D9A1C5E4F2
customer_genderstringGender of the customermale · female · other · unknown · optionalfemale
customer_citystringCity of the customer's residenceoptionalLos Angeles
customer_statestringState or province of the customer's residenceoptionalCA
customer_countrystringCountry of the customer's residenceoptionalUSA
compromise_location_typestringType of location where the card compromise occurred (e.g., ATM, POS terminal, online, merchant)ATM · POS terminal · online · merchant · unknown · optionalonline
compromise_citystringCity where the compromise occurred (if known)optionalLos Angeles
compromise_statestringState or province where the compromise occurred (if known)optionalCA
compromise_countrystringCountry where the compromise occurred (if known)optionalUSA
additional_notesstringFree-text notes about the fraud incident or investigationoptionalCNP fraud detected via au…
Numbers 2 columns
transaction_amountfloatAmount involved in the fraudulent transaction0 or more328.45
customer_ageintegerAge of the customer at the time of the fraud incident0 to 120 · optional29
Dates and times 2 columns
transaction_datetimedatetimeDate and time when the fraudulent transaction occurred2023-02-12T14:23:17Z
reported_datetimedatetimeDate and time when the fraud was reported2023-02-12T14:58:32Z
True or false 3 columns
is_card_presentbooleanIndicates whether the card was physically present during the transactionfalse
is_counterfeit_detectedbooleanIndicates if counterfeit card activity was detectedoptionalfalse
is_merchant_compromisedbooleanIndicates if the merchant was confirmed as compromisedoptionalfalse

Use it for

  • is card present81%483 of 600 rowsmean transaction amou…77.8skim…755.4CNP71.4lost…23kcoun…

    A finance dashboard

    The is_card_present rate, transaction_amount by fraud_type and a breakdown of merchant_category. Excel, Power BI or Tableau.

  • Why do the 96 counterfeit rows have a mean transaction_amount of 22,984?

    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_hash, card_type and issuer_bank to fill a screen in front of a buyer.

Not quite right?

Make it yours.

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This dataset600 rows31 columns
Yours1,000 rows31 columnsmerchant_city: UK only

blueprint · card-fraud-patterns

Behind this dataset

Same schema. As many rows as you need.

These 600 rows came out of a blueprint — 31 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
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
card-fraud-patterns

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