Finance
16 columns
200 rows
8 formats

Banking Transaction Graphs Dataset

This dataset provides detailed, interconnected banking transaction records, capturing sender and receiver relationships, transaction metadata, and anomaly flags. Designed for network analytics, it enables advanced anti-money laundering (AML) detection, fraud analysis, and financial behavior modeling by representing transactions as a directed graph. The flat structure ensures easy integration with machine learning and graph analytics tools.

opened 10 times
last updated 22 Jul 2025
by GoMask

The brief that made it

Anti-money laundering (AML) model training and evaluation

Sample rows

preview · 8 of 200 rows · all 16 columns

transaction_idstring	transaction_typestring	amountfloat	is_internationalboolean	destination_countrystring	timestampdatetime	currencystring	sender_account_idstring	receiver_account_idstring	sender_customer_idstring	receiver_customer_idstring	transaction_statusstring	origin_countrystring	channelstring	reference_notestring	is_flagged_anomalyboolean
TXN000001	payment	56.23	false	US	2024-05-15T10:24:36Z	USD	ACC00001	ACC00012	CUST005	CUST023	completed	US	online	utility bill payment	false
TXN000002	transfer	3000.5	true	FR	2024-03-27T17:10:42Z	EUR	ACC00015	ACC00022	CUST011	CUST034	pending	DE	branch	M&A fund transfer	true
TXN000003	payment	2.75	false	US	2024-01-01T00:05:10Z	USD	ACC00003	ACC00008	CUST002	CUST018	completed	US	mobile	coffee	false
TXN000004	deposit	150	false	GB	2024-02-29T11:32:21Z	GBP	ACC00019	ACC00029	CUST009	CUST041	completed	GB	ATM	salary deposit	false
TXN000005	transfer	25000	true	CA	2024-04-01T08:02:15Z	USD	ACC00020	ACC00030	CUST013	CUST045	completed	US	branch	corporate transfer	true
TXN000006	payment	100	false	DE	2024-03-05T20:45:59Z	EUR	ACC00005	ACC00015	CUST001	CUST037	completed	DE	online	subscription renewal	false
TXN000007	payment	0.99	false	IN	2024-05-30T23:11:09Z	INR	ACC00023	ACC00028	CUST015	CUST042	completed	IN	mobile	micro payment	false
TXN000008	withdrawal	250	false	CH	2024-03-19T13:00:00Z	CHF	ACC00032	ACC00033	CUST021	CUST022	completed	CH	ATM	ATM cash withdrawal	false
TXN000009	deposit	8000	false	JP	2024-04-30T17:39:12Z	JPY	ACC00034	ACC00039	CUST025	CUST031	pending	JP	ATM	payroll	false
TXN000010	payment	15	false	SG	2024-02-14T09:15:33Z	SGD	ACC00016	ACC00024	CUST016	CUST036	completed	SG	online	Valentine's gift	false
TXN000011	transfer	500000	true	SG	2024-01-15T18:05:45Z	CNY	ACC00037	ACC00040	CUST030	CUST047	completed	CN	branch	international business deal	true
TXN000012	withdrawal	120.5	false	CA	2024-05-09T07:00:00Z	CAD	ACC00041	ACC00045	CUST033	CUST049	completed	CA	ATM	cash withdrawal	false
TXN000013	transfer	1250000	true	GB	2023-12-31T23:50:10Z	USD	ACC00050	ACC00051	CUST050	CUST051	pending	US	online	year end portfolio transfer	true
TXN000014	payment	5	false	US	2024-03-01T03:30:00Z	USD	ACC00006	ACC00013	CUST003	CUST024	completed	US	mobile	test payment	false
TXN000015	payment	75.25	false	AU	2024-05-05T16:12:50Z	AUD	ACC00046	ACC00053	CUST035	CUST052	completed	AU	online	concert tickets	false
TXN000016	transfer	11000	true	DE	2024-04-16T19:43:10Z	CHF	ACC00054	ACC00058	CUST038	CUST056	completed	CH	branch	cross-border investment	true
TXN000017	payment	51.75	false	SG	2024-02-15T12:25:00Z	SGD	ACC00059	ACC00060	CUST039	CUST057	completed	SG	online	groceries	false
TXN000018	deposit	200	false	IN	2024-03-17T20:20:20Z	INR	ACC00062	ACC00063	CUST040	CUST058	completed	IN	ATM	cash deposit	false
TXN000019	transfer	120000	true	JP	2024-06-01T05:55:10Z	USD	ACC00064	ACC00065	CUST042	CUST059	completed	US	online	investment remittance	true
TXN000020	payment	0.5	false	US	2024-04-09T18:12:18Z	USD	ACC00066	ACC00067	CUST043	CUST060	completed	US	mobile	micro payment	false

What the 200 rows show

from the 200-row sample

Transfer (transaction type) stands out: 49 of its 60 rows have is_international = true, against 2 of 140 for the rest.

26%is_international = true
250.0median amount
4transaction statuses
4channels
10currencies
12origin countries

Is international rate by transaction_typeis_international = true

amount200 rows, in bands of 1M

Median 250.0, from 0.01 to 10,000,000.

destination_country200 rows · top 10 of 18 values

US48
SG28
IN19
GB17
CA15
DE15
JP14
AU12
CH9
FR8

16 columns by typefrom the column list below

string 12
float 1
datetime 1
boolean 2

Columns

16 columns in four groups

blueprint · 16 columns
column	type	description	example
Text 12 columns
`transaction_id`	string	Unique identifier for each transaction.unique	TXN000001
`currency`	string	ISO 4217 currency code used in the transaction (e.g., USD, EUR).10 currencies	USD
`sender_account_id`	string	Unique identifier for the account sending the funds.	ACC00001
`receiver_account_id`	string	Unique identifier for the account receiving the funds.	ACC00012
`sender_customer_id`	string	Unique identifier for the customer who owns the sender account.	CUST005
`receiver_customer_id`	string	Unique identifier for the customer who owns the receiver account.	CUST023
`transaction_type`	string	Type of transaction (e.g., transfer, deposit, withdrawal, payment).transfer · deposit · withdrawal · payment · other	payment
`transaction_status`	string	Current status of the transaction (e.g., completed, pending, failed, reversed).completed · pending · failed · reversed	completed
`origin_country`	string	Country code (ISO 3166-1 alpha-2) of the sender's account.12 countries · optional	US
`destination_country`	string	Country code (ISO 3166-1 alpha-2) of the receiver's account.optional	US
`channel`	string	Channel through which the transaction was initiated (e.g., online, branch, ATM, mobile).online · branch · ATM · mobile · other · optional	online
`reference_note`	string	Free-text field for transaction reference or description.up to 255 · optional	utility bill payment
Numbers 1 column
`amount`	float	Monetary value of the transaction.0 or more	56.23
Dates and times 1 column
`timestamp`	datetime	Date and time when the transaction occurred.	2024-05-15T10:24:36Z
True or false 2 columns
`is_international`	boolean	Indicates if the transaction is cross-border (true) or domestic (false).	false
`is_flagged_anomaly`	boolean	Indicates if the transaction has been flagged as anomalous or suspicious.optional	false

Use it for

A finance dashboard
The is_international rate, amount by transaction_type and a breakdown of destination_country. Excel, Power BI or Tableau.
Why do 51 of 200 rows have is_international = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Transactions200
TXN00000156.23payment
TXN0000023000.5transfer
TXN00000525000transfer
A software demo
Believable transactions with timestamp, amount and currency to fill a screen in front of a buyer.

This dataset200 rows16 columns

Yours1,000 rows16 columnsorigin_country: UK only

blueprint · banking-transaction-graphs-dataset

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

Graph edges represent monetary transfers
Cycles may indicate suspicious activity
Node attributes include account type
Transaction types: deposit, transfer, withdrawal
Temporal sequence respected for flows

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: banking-transaction-graphs-dataset

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?