Finance
16 columns
200 rows
8 formats

Peer-to-Peer Payment Patterns

This dataset provides detailed, synthetic records of peer-to-peer payment transactions, including sender and receiver information, transaction amounts, statuses, device types, and fraud indicators. It is ideal for analyzing user payment behaviors, developing risk models, and designing fintech products, offering a robust foundation for segmentation and predictive analytics.

last updated 22 Jul 2025
by GoMask

The brief that made it

User segmentation and behavioral analysis for fintech platforms

Sample rows

preview · 8 of 200 rows · all 16 columns

transaction_idstring	transaction_statusstring	amountfloat	sender_account_verifiedboolean	receiver_countrystring	sender_user_idstring	receiver_user_idstring	transaction_datetimedatetime	currencystring	payment_methodstring	receiver_account_verifiedboolean	sender_countrystring	device_typestring	transaction_notestring	is_first_transactionboolean	fraud_flagboolean
T1000001	completed	153.75	true	US	U10001	U10002	2023-12-31T21:57:02Z	USD	debit_card	true	US	mobile	Dinner split	false	false
T1000002	pending	12	false	US	U10003	U10004	2024-02-28T16:33:10Z	USD	wallet_balance	false	US	mobile	blank	true	false
T1000003	completed	725.4	true	FR	U10005	U10006	2024-04-15T08:22:17Z	EUR	credit_card	false	DE	desktop	Consulting invoice	false	false
T1000004	completed	37.18	true	US	U10007	U10008	2024-06-30T19:48:11Z	USD	wallet_balance	true	US	mobile	Gift	false	false
T1000005	failed	0.5	false	US	U10009	U10010	2023-11-21T13:10:35Z	USD	wallet_balance	false	US	desktop	Test	true	true
T1000006	completed	2500	true	US	U10011	U10012	2023-12-29T10:15:47Z	USD	bank_transfer	true	US	desktop	Rent payment	false	false
T1000007	pending	18.25	false	GB	U10013	U10014	2024-01-15T06:36:53Z	GBP	wallet_balance	false	GB	mobile	blank	true	false
T1000008	failed	3	false	US	U10015	U10016	2023-10-30T22:10:07Z	USD	debit_card	false	US	mobile	blank	true	true
T1000009	completed	11000	true	JP	U10017	U10018	2024-11-30T18:41:13Z	JPY	credit_card	false	JP	desktop	Bonus	false	false
T1000010	completed	70	true	US	U10019	U10020	2024-05-31T20:15:22Z	USD	debit_card	false	US	mobile	Movie tickets	false	false
T1000011	completed	205	true	US	U10021	U10022	2023-08-15T03:10:55Z	CAD	wallet_balance	true	CA	mobile	Gift for birthday	false	false
T1000012	reversed	150000	false	MX	U10023	U10024	2021-12-31T23:59:59Z	USD	other	false	US	other	EDGE CASE: Limit exceeded	true	true
T1000013	completed	1289.67	true	NL	U10025	U10026	2022-09-25T10:14:22Z	EUR	bank_transfer	false	DE	desktop	Payment for art	false	false
T1000014	completed	41.8	true	US	U10027	U10028	2023-01-31T16:03:13Z	USD	debit_card	true	US	mobile	Lunch	false	false
T1000015	pending	0.01	false	US	U10029	U10030	2023-08-19T21:23:43Z	USD	wallet_balance	false	US	desktop	blank	true	false
T1000016	completed	4104.51	true	US	U10031	U10032	2023-11-30T14:45:59Z	AUD	bank_transfer	false	AU	desktop	Tuition payment	false	false
T1000017	completed	78.85	true	US	U10033	U10034	2024-03-31T11:41:22Z	USD	wallet_balance	true	US	mobile	Groceries	false	false
T1000018	completed	15999.99	true	US	U10035	U10036	2024-06-30T20:57:17Z	GBP	bank_transfer	false	GB	tablet	Business transfer	false	false
T1000019	completed	480	true	US	U10037	U10038	2024-07-31T09:29:26Z	USD	debit_card	false	US	mobile	Monthly rent	false	false
T1000020	reversed	7000000	false	US	U10039	U10040	2025-12-31T23:53:09Z	CNY	other	false	CN	other	EDGE CASE: Incorrect recipient	false	true

What the 200 rows show

from the 200-row sample

Completed (transaction status) stands out: 136 of its 139 rows have sender_account_verified = true, against 7 of 61 for the rest.

72%sender_account_verified = true
200.0median amount
4device types
5payment methods
12currencies
13sender countries

Sender account verified rate by transaction_statussender_account_verified = true

amount200 rows, in bands of 1M

Median 200.0, from 0.01 to 10,000,000.

receiver_country200 rows · top 10 of 22 values

US138
FR9
CA7
DE7
GB5
NZ5
MX4
IN4
JP3
NL3

16 columns by typefrom the column list below

string 10
float 1
datetime 1
boolean 4

Columns

16 columns in four groups

blueprint · 16 columns
column	type	description	example
Text 10 columns
`transaction_id`	string	Unique identifier for each P2P payment transactionunique	T1000001
`sender_user_id`	string	Unique identifier for the user sending the payment	U10001
`receiver_user_id`	string	Unique identifier for the user receiving the payment	U10002
`currency`	string	ISO 4217 currency code for the transaction (e.g., USD, EUR)12 currencies	USD
`transaction_status`	string	Status of the transaction (e.g., completed, pending, failed, reversed)completed · pending · failed · reversed	completed
`payment_method`	string	Payment method used for the transaction (e.g., bank_transfer, debit_card, credit_card, wallet_balance)5 values	debit_card
`sender_country`	string	Country code (ISO 3166-1 alpha-2) of the sender	US
`receiver_country`	string	Country code (ISO 3166-1 alpha-2) of the receiver	US
`device_type`	string	Device type used to initiate the transaction (e.g., mobile, desktop, tablet)mobile · desktop · tablet · other · optional	mobile
`transaction_note`	string	Optional note or memo attached to the paymentup to 255 · optional	Dinner split
Numbers 1 column
`amount`	float	Monetary value of the transaction in the specified currency0.01 or more	153.75
Dates and times 1 column
`transaction_datetime`	datetime	Date and time when the payment transaction occurred	2023-12-31T21:57:02Z
True or false 4 columns
`sender_account_verified`	boolean	Indicates if the sender's account is verified	true
`receiver_account_verified`	boolean	Indicates if the receiver's account is verified	true
`is_first_transaction`	boolean	Indicates if this is the sender's first transactionoptional	false
`fraud_flag`	boolean	Indicates if the transaction is flagged as potentially fraudulentoptional	false

Use it for

A finance dashboard
The sender_account_verified rate, amount by transaction_status and a breakdown of receiver_country. Excel, Power BI or Tableau.
Why do 143 of 200 rows have sender_account_verified = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Transactions200
T1000001153.75completed
T100000212pending
T1000003725.4completed
A software demo
Believable transactions with sender_user_id, receiver_user_id and transaction_datetime to fill a screen in front of a buyer.

This dataset200 rows16 columns

Yours1,000 rows16 columnssender_country: UK only

blueprint · peer-to-peer-payment-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 16 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

Track sender and recipient IDs, transaction values
Vary transaction frequency by user segment
Include device and channel metadata
Flag round-trip or circular payment patterns

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: peer-to-peer-payment-patterns

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

A finance dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Finance

What should your data show?