Financial Transaction Fraud Labels

This dataset contains detailed records of financial transactions, including customer demographics, merchant information, device characteristics, and explicit fraud labels. It is designed for machine learning-driven fraud detection, risk analysis, and real-time alerting, enabling robust analytics and model development for financial institutions and payment processors.

  • last updated 21 Jul 2025
  • by GoMask
The brief that made it

Training and evaluating machine learning models for fraud detection

Sample rows

preview · 8 of 200 rows · all 19 columns
transaction_idstringcustomer_genderstringamountfloatis_fraudbooleancustomer_countrystringtransaction_datetimedatetimecurrencystringcustomer_idstringcustomer_ageintegermerchant_idstringmerchant_namestringmerchant_categorystringmerchant_countrystringdevice_idstringdevice_typestringdevice_osstringip_addressstringfraud_label_sourcestringfraud_report_datetimedatetime
TXN000001male156.27falseUS2024-03-15T11:14:22ZUSDCUST00000129MERCHANT001Tech OutletelectronicsUSDEV1001mobileiOS192.168.1.102blankblank
TXN000002female47.99falseGB2023-12-24T19:42:05ZGBPCUST00000233MERCHANT002Book NookbooksGBDEV1002desktopWindows51.6.163.44blankblank
TXN000003male1899.2falseDE2022-11-07T08:10:11ZEURCUST00000354MERCHANT003Elegant JewelersjewelryDEDEV1003desktopLinux88.130.11.199blankblank
TXN000004unknown0trueblank2024-01-01T00:05:48ZJPYCUST0000040MERCHANT004blanktestblankblankblankblankblankml_model2024-01-01T13:32:17Z
TXN000005female57.45falseUS2022-07-09T14:03:45ZUSDCUST00000522MERCHANT005Fresh MarketgroceryUSDEV1004mobileAndroid10.0.0.15blankblank
TXN000006male12003.3falseCA2023-05-30T23:42:12ZCADCUST00000663MERCHANT006AutoPro ServiceautomotiveCADEV1005desktopWindows24.114.98.200blankblank
TXN000007female35.99falseDE2023-02-14T17:25:13ZEURCUST00000725MERCHANT007Fine DiningrestaurantDEDEV1006tabletAndroid91.43.22.14blankblank
TXN000008male320falseUS2022-12-25T09:22:09ZUSDCUST00000844MERCHANT008TravelWiztravelUSDEV1007desktopmacOS172.16.0.111blankblank

What the 200 rows show

from the 200-row sample

Unknown (customer gender) stands out: 29 of its 30 rows have is_fraud = true, against 0 of 170 for the rest.

  • 14%is_fraud = true
  • 120.0median amount
  • 4fraud label sources
  • 5device types
  • 7device oses
  • 12currencies
Is fraud rate by customer_genderis_fraud = true
0%50%100%0%male0 of 740%female0 of 830%other0 of 1397%unknown29 of 30

Unknown (customer gender)'s mean amount is 716,477, against 3,762 for male, 8,612 for female and 28.3 for other.

amount200 rows, in bands of 500k
010020019501000000402.5M5Mamount →

Median 120.0, from 0.0 to 5,000,000.

customer_country183 rows with a value · 17 left blank
  1. US57
  2. DE17
  3. IN16
  4. CA14
  5. GB12
  6. AU12
  7. CN12
  8. SE10
  9. JP9
  10. CH9
19 columns by typefrom the column list below
  • string 14
  • integer 1
  • float 1
  • datetime 2
  • boolean 1

Columns

19 columns in four groups
blueprint · 19 columns
columntypedescriptionexample
Text 14 columns
transaction_idstringUnique identifier for each financial transactionuniqueTXN000001
currencystringISO 4217 currency code used for the transaction (e.g., USD, EUR)12 currenciesUSD
customer_idstringUnique identifier for the customer performing the transactionCUST000001
customer_genderstringGender of the customermale · female · other · unknown · optionalmale
customer_countrystringCountry of the customer at the time of transaction (ISO 3166-1 alpha-2 code)optionalUS
merchant_idstringUnique identifier for the merchant receiving the transactionMERCHANT001
merchant_namestringName of the merchantoptionalTech Outlet
merchant_categorystringCategory or type of merchant (e.g., grocery, electronics, travel)optionalelectronics
merchant_countrystringCountry where the merchant is located (ISO 3166-1 alpha-2 code)optionalUS
device_idstringUnique identifier for the device used in the transactionoptionalDEV1001
device_typestringType of device used (e.g., mobile, desktop, tablet, POS terminal)mobile · desktop · tablet · pos · other · optionalmobile
device_osstringOperating system of the device (e.g., Android, iOS, Windows, Linux)7 oses · optionaliOS
ip_addressstringIP address from which the transaction was initiated (IPv4 or IPv6)optional192.168.1.102
fraud_label_sourcestringSource of the fraud label (e.g., manual review, ML model, chargeback)manual_review · ml_model · chargeback · other · optionalml_model
Numbers 2 columns
amountfloatMonetary value of the transaction0 or more156.27
customer_ageintegerAge of the customer at the time of transaction0 to 120 · optional29
Dates and times 2 columns
transaction_datetimedatetimeDate and time when the transaction occurred2024-03-15T11:14:22Z
fraud_report_datetimedatetimeDate and time when the fraud was reported or confirmedoptional2024-01-01T13:32:17Z
True or false 1 column
is_fraudbooleanIndicates whether the transaction was labeled as fraudulent (true) or not (false)false

Use it for

  • is fraud14%29 of 200 rowsmean amount by custom…3.8kmale8.6kfema…28.3other716.5kunkn…

    A finance dashboard

    The is_fraud rate, amount by customer_gender and a breakdown of customer_country. Excel, Power BI or Tableau.

  • Why do 29 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 transaction_datetime, amount and currency to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 19 columns, your size and your rules. See 20 rows before you pay.

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1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows19 columns
Yours1,000 rows19 columnscustomer_country: UK only

blueprint · financial-transaction-fraud-labels

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 19 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
  • Each transaction labeled as fraudulent or not
  • Include merchant category, amount, and timestamp
  • Simulate cross-border and high-risk patterns
  • User device and location data included
  • Customer profiles anonymized
Rows
Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
financial-transaction-fraud-labels

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