Finance
23 columns
200 rows
8 formats

Cross-Border Payment Transactions

This dataset provides detailed, simulated records of international payment transactions, including sender and recipient information, transaction amounts, currencies, risk scores, and AML/fraud flagging. It is ideal for developing, testing, and benchmarking anti-money laundering, fraud detection, and risk assessment models in cross-border financial environments.

last updated 22 Jul 2025
by GoMask

The brief that made it

Training and evaluating AML and fraud detection algorithms

Sample rows

preview · 8 of 200 rows · all 23 columns

transaction_idstring	transaction_statusstring	risk_scorefloat	is_cross_borderboolean	recipient_bank_countrystring	transaction_datetimedatetime	amountfloat	currencystring	sender_account_idstring	sender_namestring	sender_bank_namestring	sender_bank_countrystring	sender_countrystring	recipient_account_idstring	recipient_namestring	recipient_bank_namestring	recipient_countrystring	payment_methodstring	purpose_codestring	exchange_ratefloat	fee_amountfloat	is_flaggedboolean	flag_reasonstring
TXN2024010001	completed	0.16	true	SE	2024-01-03T09:12:41Z	1315.75	USD	ACC10001	Carlos Mendoza	Bank of America	US	US	ACC20001	Emma Johansson	Handelsbanken	SE	swift	invoice_payment	10.42	25.5	false	blank
TXN2024010002	completed	0.08	true	FR	2024-01-04T16:44:55Z	85.2	EUR	ACC10002	Lina Schmidt	Deutsche Bank	DE	DE	ACC20002	Sophie Laurent	BNP Paribas	FR	sepa	ecommerce_payment	1.07	1.4	false	blank
TXN2024010003	flagged	0.91	true	CN	2024-01-06T10:17:02Z	12000	USD	ACC10003	Sarah Lee	Wells Fargo	US	US	ACC20003	Minghao Zhang	ICBC	CN	swift	investment	7.13	120	true	Large amount sent to new overseas account
TXN2024010004	completed	0.09	true	IT	2024-01-07T12:02:56Z	59.99	EUR	ACC10004	Jan Novak	Ceska Sporitelna	CZ	CZ	ACC20004	Marta Rossi	UniCredit	IT	sepa	subscription	1.13	0.85	false	blank
TXN2024010005	failed	0.04	true	BR	2024-01-08T19:14:11Z	0.03	USD	ACC10005	Lucas Walker	Chase Bank	US	US	ACC20005	Diego Silva	Banco do Brasil	BR	wire_transfer	micro_test	4.99	0.1	true	Repeated failed micro-amount test transactions
TXN2024010006	completed	0.14	true	KR	2024-01-09T11:49:21Z	200	GBP	ACC10006	Leah Patel	HSBC UK	GB	GB	ACC20006	Noah Kim	Kookmin Bank	KR	swift	services	1.64	6.95	false	blank
TXN2024010007	pending	0.12	true	ES	2024-01-10T08:22:18Z	480	EUR	ACC10007	Eva Müller	Commerzbank	DE	DE	ACC20007	Isabella Garcia	Banco Santander	ES	sepa	invoice	1.11	2.9	false	blank
TXN2024010008	completed	0.07	true	NZ	2024-01-11T13:35:57Z	45.7	AUD	ACC10008	Oliver Smith	Commonwealth Bank	AU	AU	ACC20008	David Lee	ANZ	NZ	wire_transfer	gift	1.08	1.2	false	blank
TXN2024010009	pending	0.83	true	GB	2024-01-12T15:01:13Z	985000	CHF	ACC10009	Stefan Keller	Credit Suisse	CH	CH	ACC20009	Grace Brown	UBS	GB	swift	business_acquisition	0.91	500	true	Large value flagged for corporate AML review
TXN2024010010	completed	0.07	false	GB	2024-01-13T17:48:44Z	17.5	GBP	ACC10010	Amelia Jones	NatWest	GB	GB	ACC20010	Daniel Evans	Barclays	GB	card	subscription	blank	0.5	false	blank
TXN2024010011	completed	0.13	true	VN	2024-01-14T07:10:28Z	700	USD	ACC10011	Anna Ivanova	Citibank	US	US	ACC20011	Nguyen Van Binh	Vietcombank	VN	swift	salary	24350	10	false	blank
TXN2024010012	completed	0.12	true	FR	2024-01-15T14:23:35Z	110	EUR	ACC10012	Maria Fernandez	Banco Sabadell	ES	ES	ACC20012	Pierre Dubois	BNP Paribas	FR	sepa	reimbursement	1.15	1.7	false	blank
TXN2024010013	completed	0.31	true	MX	2024-01-16T20:05:19Z	4500	USD	ACC10013	Jason Miller	JPMorgan Chase	US	US	ACC20013	Manuel Ortega	Banco Santander	MX	swift	property_purchase	17.11	45	false	blank
TXN2024010014	completed	0.12	true	CA	2024-01-17T11:54:27Z	750	USD	ACC10014	Zoe Brown	Bank of America	US	US	ACC20014	Anthony Young	Scotiabank	CA	wire_transfer	family_support	1.33	12	false	blank
TXN2024010015	completed	0.03	false	US	2024-01-18T18:37:02Z	15.5	USD	ACC10015	Michael Johnson	Wells Fargo	US	US	ACC20015	John Smith	Chase Bank	US	card	ecommerce_payment	blank	0.95	false	blank
TXN2024010016	failed	0.01	true	NZ	2024-01-19T13:29:55Z	5	JPY	ACC10016	Hiroshi Tanaka	Mizuho Bank	JP	JP	ACC20016	Emily Clark	ANZ	NZ	swift	test_payment	0.0083	0.12	false	blank
TXN2024010017	completed	0.11	true	GB	2024-01-20T09:45:31Z	250	USD	ACC10017	Fatima Al-Farsi	Emirates NBD	AE	AE	ACC20017	James Anderson	Barclays	GB	swift	services	0.27	8.25	false	blank
TXN2024010018	completed	0.29	true	US	2024-01-21T22:58:13Z	0.2	BTC	ACC10018	Wei Liu	Binance	CN	CN	ACC20018	Alex Kim	Coinbase	US	crypto	crypto_transfer	0.000023	0.0005	false	blank
TXN2024010019	failed	0.03	true	FR	2024-01-22T06:12:43Z	2.5	EUR	ACC10019	Giulia Bianchi	Intesa Sanpaolo	IT	IT	ACC20019	Luc Dubois	BNP Paribas	FR	sepa	micro	1.07	0.15	false	blank
TXN2024010020	completed	0.06	false	GB	2024-01-23T11:19:28Z	320	GBP	ACC10020	James Wilson	Barclays	GB	GB	ACC20020	Laura Evans	HSBC UK	GB	wire_transfer	rent	blank	5.4	false	blank

What the 200 rows show

from the 200-row sample

Flagged (transaction status) stands out: mean risk_score is 0.95, against 0.13 for the rest.

80%is_cross_border = true
0.12median risk_score
6payment methods
11currencies
25purpose codes
26sender bank countries

Mean risk_score by transaction_status200 rows

risk_score200 rows, in bands of 0.1

Median 0.12, from 0.01 to 1.0.

recipient_bank_country200 rows · top 10 of 30 values

US27
GB16
ES12
CA12
DE12
CN11
BR11
FR9
RU9
IT8

23 columns by typefrom the column list below

string 16
float 4
datetime 1
boolean 2

Columns

23 columns in four groups

blueprint · 23 columns
column	type	description	example
Text 16 columns
`transaction_id`	string	Unique identifier for the payment transactionunique	TXN2024010001
`currency`	string	ISO 4217 currency code for the transaction (e.g., USD, EUR, JPY)11 currencies	USD
`sender_account_id`	string	Unique identifier for the sender's account	ACC10001
`sender_name`	string	Full name of the sender	Carlos Mendoza
`sender_bank_name`	string	Name of the sender's bank	Bank of America
`sender_bank_country`	string	Country where the sender's bank is located (ISO 3166-1 alpha-2 code)	US
`sender_country`	string	Country of residence of the sender (ISO 3166-1 alpha-2 code)	US
`recipient_account_id`	string	Unique identifier for the recipient's account	ACC20001
`recipient_name`	string	Full name of the recipient	Emma Johansson
`recipient_bank_name`	string	Name of the recipient's bank	Handelsbanken
`recipient_bank_country`	string	Country where the recipient's bank is located (ISO 3166-1 alpha-2 code)	SE
`recipient_country`	string	Country of residence of the recipient (ISO 3166-1 alpha-2 code)	SE
`payment_method`	string	Method used for the payment (e.g., wire_transfer, swift, sepa, card, crypto)6 values	swift
`transaction_status`	string	Current status of the transactionpending · completed · failed · cancelled · flagged	completed
`purpose_code`	string	Code or description indicating the purpose of the transaction (e.g., salary, invoice, gift, investment)optional	invoice_payment
`flag_reason`	string	Reason for flagging the transaction, if applicableoptional	Large amount sent to new …
Numbers 4 columns
`amount`	float	Monetary value of the transaction in the specified currency0.01 or more	1315.75
`exchange_rate`	float	Exchange rate applied if the transaction involved currency conversion0 or more · optional	10.42
`fee_amount`	float	Total transaction fee charged in the transaction currency0 or more · optional	25.5
`risk_score`	float	Automated risk score assigned to the transaction (0.0-1.0, where 1.0 is highest risk)0 to 1 · optional	0.16
Dates and times 1 column
`transaction_datetime`	datetime	Date and time when the transaction was initiated	2024-01-03T09:12:41Z
True or false 2 columns
`is_cross_border`	boolean	Indicates if the transaction is cross-border (sender and recipient countries differ)	true
`is_flagged`	boolean	Indicates if the transaction was flagged for review (e.g., for AML or fraud)	false

Use it for

A finance dashboard
The is_cross_border rate, risk_score by transaction_status and a breakdown of recipient_bank_country. Excel, Power BI or Tableau.
Why do the 17 flagged rows have a mean risk_score of 0.95?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Transactions200
TXN20240100010.16completed
TXN20240100020.08completed
TXN20240100100.07completed
A software demo
Believable transactions with transaction_datetime, amount and currency to fill a screen in front of a buyer.

This dataset200 rows23 columns

Yours1,000 rows23 columnssender_bank_country: UK only

blueprint · cross-border-payment-transactions

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 23 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

Capture sender, receiver, amount, currency
Flag suspicious transactions based on rule-based triggers
Include real-world currency fluctuations
Simulate various payment channels (bank, fintech, crypto)

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: cross-border-payment-transactions

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?