Peer-to-Peer Payment Analysis

This dataset provides detailed records of peer-to-peer payment transactions, including sender and receiver information, transaction amounts, payment methods, risk flags, and product opportunity indicators. It enables comprehensive analysis of payment trends, user behaviors, geographic patterns, and identification of risky or innovative financial activities for product development.

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

Trend analysis of peer-to-peer payment behaviors

Sample rows

preview · 8 of 200 rows · all 19 columns
transaction_idstringproduct_opportunity_typestringamountfloatrisk_flagbooleansender_location_countrystringsender_user_idstringreceiver_user_idstringtransaction_datetimedatetimecurrencystringpayment_methodstringtransaction_statusstringrisk_reasonstringtransaction_notestringsender_location_citystringsender_location_statestringreceiver_location_citystringreceiver_location_statestringreceiver_location_countrystringproduct_opportunity_flagboolean
TXN000001rewards15.5falseUSUSR1000001USR10000022023-07-04T14:22:13ZUSDwalletcompletedblankLunch splitSan FranciscoCAOaklandCAUStrue
TXN000002insurance50000trueGBUSR1000003USR10000042023-12-31T23:58:45ZGBPbank_transferpendingLarge transfer amountYear-end bonus paymentLondonLondonManchesterManchesterGBtrue
TXN000003blank0.99trueUSUSR1000005USR10000062023-01-01T09:14:23ZUSDwalletcompletedUnusually low amountTest transactionNew YorkNYBrooklynNYUSfalse
TXN000004microloan120falseDEUSR1000007USR10000082023-03-15T17:30:10ZEURdebit_cardcompletedblankDinner partyBerlinBerlinMunichBavariaDEtrue
TXN000005blank50falseAUUSR1000009USR10000102023-02-14T10:18:07ZAUDcredit_cardcompletedblankValentine's Day giftSydneyNSWMelbourneVICAUfalse
TXN000006insurance123456.78trueUSUSR1000011USR10000122023-08-21T11:03:29ZUSDbank_transferpendingInvestment flagged for reviewAcquisition paymentChicagoILBostonMAUStrue
TXN000007blank5falseINUSR1000013USR10000142023-09-05T15:44:11ZINRwalletcompletedblankCoffee splitMumbaiMHPuneMHINfalse
TXN000008rewards900falseESUSR1000015USR10000162023-04-17T19:22:47ZEURdebit_cardcompletedblankVacation expenseMadridMadridBarcelonaCatalunyaEStrue

What the 200 rows show

from the 200-row sample

Insurance (product opportunity type) stands out: 29 of its 29 rows have risk_flag = true, against 2 of 91 for the rest.

  • 37%risk_flag = true
  • 80.3median amount
  • 4transaction statuses
  • 5payment methods
  • 9currencies
  • 18receiver location countries
Risk flag rate by product_opportunity_typerisk_flag = true
0%50%100%0%rewards0 of 475%microloan2 of 44100%insurance29 of 29

Insurance (product opportunity type)'s mean amount is 1,402,459, against 117.9 for rewards and 949.0 for microloan.

amount200 rows, in bands of 1M
010020019321001000305M10Mamount →

Median 80.3, from 0.01 to 10,000,000.

sender_location_country197 rows with a value · 3 left blank
  1. US71
  2. CA19
  3. IN18
  4. AU16
  5. GB15
  6. DE9
  7. CH9
  8. NZ7
  9. IT6
  10. ES5
19 columns by typefrom the column list below
  • string 15
  • float 1
  • datetime 1
  • boolean 2

Columns

19 columns in four groups
blueprint · 19 columns
columntypedescriptionexample
Text 15 columns
transaction_idstringUnique identifier for each peer-to-peer payment transactionuniqueTXN000001
sender_user_idstringUnique identifier of the user sending the paymentUSR1000001
receiver_user_idstringUnique identifier of the user receiving the paymentUSR1000002
currencystringCurrency code used in the transaction (e.g., USD, EUR)9 currenciesUSD
payment_methodstringMethod used for payment (e.g., bank_transfer, credit_card, wallet)6 valueswallet
transaction_statusstringStatus of the transaction (e.g., completed, pending, failed, reversed)completed · pending · failed · reversedcompleted
risk_reasonstringReason for risk flag (if applicable); empty if not flaggedoptionalLarge transfer amount
transaction_notestringOptional note or memo attached to the transactionup to 500 · optionalLunch split
sender_location_citystringCity of the sender at the time of transactionoptionalSan Francisco
sender_location_statestringState or region of the sender at the time of transactionoptionalCA
sender_location_countrystringCountry of the sender at the time of transactionoptionalUS
receiver_location_citystringCity of the receiver at the time of transactionoptionalOakland
receiver_location_statestringState or region of the receiver at the time of transactionoptionalCA
receiver_location_countrystringCountry of the receiver at the time of transactionoptionalUS
product_opportunity_typestringType of financial product opportunity identified (e.g., microloan, insurance, rewards); empty if not flagged3 types · optionalrewards
Numbers 1 column
amountfloatMonetary value of the transaction0.01 or more15.5
Dates and times 1 column
transaction_datetimedatetimeDate and time when the transaction occurred2023-07-04T14:22:13Z
True or false 2 columns
risk_flagbooleanIndicates if the transaction was flagged as riskyfalse
product_opportunity_flagbooleanIndicates if the transaction suggests an opportunity for a new financial productoptionaltrue

Use it for

  • risk flag37%74 of 200 rowsmean amount by produc…117.9rewards949.0microl…1.4Minsura…

    A finance dashboard

    The risk_flag rate, amount by product_opportunity_type and a breakdown of sender_location_country. Excel, Power BI or Tableau.

  • Why do 74 of 200 rows have risk_flag = 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 sender_user_id, receiver_user_id and transaction_datetime 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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This dataset200 rows19 columns
Yours1,000 rows19 columnssender_location_city: UK only

blueprint · peer-to-peer-payment-analysis

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 sender and receiver profiles
  • Record transaction amount and timestamp
  • Track payment platform/channel
  • Flag unusually large transactions
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
peer-to-peer-payment-analysis

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