Merchant Transaction Risk Scoring

This dataset provides granular records of merchant transactions scored for risk, including transaction details, merchant and customer identifiers, risk assessment metrics, and compliance/fraud flags. It enables financial institutions to identify suspicious activity, support regulatory compliance, and optimize fraud detection workflows with actionable insights.

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

Automated fraud detection and prevention for merchant transactions

Sample rows

preview · 8 of 200 rows · all 19 columns
transaction_idstringrisk_levelstringtransaction_amountfloatcompliance_flagbooleanreviewed_bystringmerchant_idstringmerchant_namestringmerchant_categorystringtransaction_datetimedatetimecurrencystringpayment_methodstringcustomer_idstringcustomer_countrystringrisk_scorefloatfraud_flagbooleanreview_statusstringreview_datetimedatetimemerchant_countrystringrisk_reasonstring
TX0000001low15.99falsesystem_autoMRC001BrightMartretail2024-05-03T10:16:29ZUSDcredit_cardCUST1001US0.08falsecleared2024-05-03T10:18:45ZUSTransaction amount within typical range; no suspicious activity.
TX0000002low34.25falsesystem_autoMRC002Gourmet Foodsfood2024-05-03T12:09:54ZUSDdebit_cardCUST1002US0.13falsecleared2024-05-03T12:12:13ZUSRegular debit card purchase; same-country customer.
TX0000003high1200.75truereview_team_eastMRC003TechZoneelectronics2024-05-03T14:22:38ZUSDbank_transferCUST1003CA0.75falseescalated2024-05-03T14:38:12ZUSLarge electronics purchase; cross-border bank transfer.
TX0000004high899.99trueglobal_review_teamMRC004TravelNowtravel2024-05-03T07:10:05ZGBPbank_transferCUST1004DE0.81falseescalated2024-05-03T07:26:30ZGBHigh-value travel booking; cross-border transaction.
TX0000005high120.5truereview_team_franceMRC005FuelExpressfuel2024-05-04T18:44:20ZEURdebit_cardCUST1005FR0.71falseescalated2024-05-04T19:01:19ZDEHigh fuel spend; foreign customer detected.
TX0000006critical49999.99truefraud_team_globalMRC006SoftLogicsoftware2024-05-04T09:53:42ZUSDbank_transferCUST1006IN0.96trueconfirmed_fraud2024-05-04T10:28:33ZUSExceptionally high bank transfer; flagged for manual review.
TX0000007low22falsesystem_autoMRC001BrightMartretail2024-05-04T11:21:53ZUSDdebit_cardCUST1007US0.09falsecleared2024-05-04T11:23:17ZUSRoutine retail purchase using debit card.
TX0000008low62.45falsesystem_autoMRC007FreshMartgrocery2024-05-04T08:06:40ZUSDcredit_cardCUST1008US0.12falsecleared2024-05-04T08:09:11ZUSGrocery purchase; regular customer profile.

What the 200 rows show

from the 200-row sample

Critical (risk level) stands out: mean transaction_amount is 51,350, against 276.9 for the rest.

  • 45%compliance_flag = true
  • 44.4median transaction_amount
  • 4review statuses
  • 6currencies
  • 6payment methods
  • 8merchant categories
Mean transaction_amount by risk_level200 rows
040k80k20.2low100 rows68.9medium16 rows768.5high59 rows51,350critical25 rows
transaction_amount200 rows, in bands of 10k
095190185110100039050k100ktransaction_amount →

Median 44.4, from 0.0 to 100,000.

reviewed_by184 rows with a value · 16 left blank
  1. system_auto100
  2. global_review_team18
  3. review_team_alpha9
  4. fraud_team_bravo7
  5. review_team_europe7
  6. review_team_north5
  7. review_team_eu5
  8. fraud_team_global4
  9. fraud_team_us4
  10. review_team_france3
19 columns by typefrom the column list below
  • string 13
  • float 2
  • datetime 2
  • boolean 2

Columns

19 columns in four groups
blueprint · 19 columns
columntypedescriptionexample
Text 13 columns
transaction_idstringUnique identifier for each merchant transactionuniqueTX0000001
merchant_idstringUnique identifier for the merchant involved in the transactionMRC001
merchant_namestringName of the merchantBrightMart
merchant_categorystringCategory or industry of the merchant (e.g., retail, food, electronics)8 categoriesretail
currencystringCurrency code used for the transaction (ISO 4217 format, e.g., USD, EUR)6 currenciesUSD
payment_methodstringMethod of payment used for the transaction (e.g., credit_card, debit_card, bank_transfer, cash)6 valuescredit_card
customer_idstringUnique identifier for the customer making the transactionCUST1001
customer_countrystringCountry of the customer involved in the transactionUS
risk_levelstringCategorical risk level assigned to the transaction (e.g., low, medium, high, critical)low · medium · high · criticallow
risk_reasonstringShort description or reason for the assigned risk score (e.g., suspicious pattern, large amount, flagged merchant)optionalRoutine grocery purchase
review_statusstringStatus of transaction review (e.g., pending, cleared, escalated, confirmed_fraud)pending · cleared · escalated · confirmed_fraudcleared
reviewed_bystringIdentifier for the analyst or system that reviewed the transactionoptionalsystem_auto
merchant_countrystringCountry where the merchant is registered or operates8 countriesUS
Numbers 2 columns
transaction_amountfloatTotal monetary value of the transaction0 or more15.99
risk_scorefloatCalculated risk score for the transaction (e.g., 0.0 to 1.0, where higher indicates greater risk)0 to 10.08
Dates and times 2 columns
transaction_datetimedatetimeDate and time when the transaction occurred2024-05-03T10:16:29Z
review_datetimedatetimeDate and time when the transaction was reviewedoptional2024-05-03T10:18:45Z
True or false 2 columns
compliance_flagbooleanIndicates if the transaction triggered a compliance alert or requires reviewfalse
fraud_flagbooleanIndicates if the transaction is suspected or confirmed as fraudulentfalse

Use it for

  • compliance flag45%90 of 200 rowsmean transaction amou…20.2low68.9medi…768.5high51.4kcrit…

    A finance dashboard

    The compliance_flag rate, transaction_amount by risk_level and a breakdown of reviewed_by. Excel, Power BI or Tableau.

  • Why do the 25 critical rows have a mean transaction_amount of 51,350?

    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 merchant_id, merchant_name and merchant_category 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.

Preview 20 rows free

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 · merchant-transaction-risk-scoring

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
  • Log transaction amount and location
  • Include merchant category
  • Flag repeat high-value transactions
  • Score based on historical risk
  • Exclude government merchants
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
merchant-transaction-risk-scoring

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