Finance
16 columns
200 rows
8 formats

Financial Transaction Pattern Analysis

This dataset provides granular financial transaction records enriched with customer segmentation, merchant categorization, and risk scoring. It enables banks and fintechs to analyze spending patterns, detect fraud, and optimize product offerings for targeted customer profiles. The dataset is ideal for risk management, customer analytics, and personalized financial services.

last updated 20 Aug 2025
by GoMask

The brief that made it

Customer segmentation and behavioral analysis

Sample rows

preview · 8 of 200 rows · all 16 columns

transaction_idstring	transaction_typestring	risk_scorefloat	is_fraud_suspectedboolean	merchant_namestring	customer_idstring	transaction_datedatetime	transaction_amountfloat	currencystring	merchant_idstring	merchant_categorystring	payment_methodstring	location_citystring	location_statestring	location_countrystring	customer_segmentstring
TXN-9D2M1BJC	Purchase	0.26	false	Apple Store	CUST-RF8Z72X	2022-11-15T14:22:32Z	11245.85	USD	MERCH-001	Retail	Credit Card	San Francisco	CA	USA	High Spender
TXN-KL2V7G4X	Purchase	0.03	false	Tim Hortons	CUST-GF3Z11K	2023-06-20T09:08:14Z	45.39	CAD	MERCH-013	Dining	Debit Card	Toronto	ON	CAN	Saver
TXN-7Q1X6V29	Purchase	0.13	false	Amazon.com	CUST-NQ9T2ZW	2024-01-25T17:45:09Z	71.2	USD	MERCH-014	Retail	Mobile Payment	Seattle	WA	USA	Online Shopper
TXN-3W9C2DBE	Purchase	0.47	false	Radisson Blu	CUST-ZP1H7DR	2023-02-11T12:36:32Z	8600	EUR	MERCH-021	Travel	Credit Card	Berlin	BE	DEU	Frequent Traveler
TXN-6S8J4V1Q	Payment	0.02	false	PG&E	CUST-QW7Z2CV	2021-12-22T19:14:05Z	187.56	USD	MERCH-005	Utilities	ACH	Sacramento	CA	USA	Saver
TXN-XC3L9B2V	Purchase	0.06	false	Starbucks Japan	CUST-HF6T3JY	2023-10-28T11:00:00Z	681.05	JPY	MERCH-022	Dining	Credit Card	Tokyo	TK	JPN	Occasional User
TXN-GH5F2K8N	Purchase	0.73	true	Rolex Boutique	CUST-JK6Z41F	2022-04-18T16:21:40Z	10567.9	CHF	MERCH-009	Retail	Credit Card	Geneva	GE	CHE	High Spender
TXN-4D2X5FJM	Fee	0.04	false	Netflix	CUST-NQ9T2ZW	2023-07-17T20:12:13Z	13.99	USD	MERCH-017	Entertainment	ACH	Los Angeles	CA	USA	Online Shopper
TXN-VT4N8Q2Y	Transfer	0.62	true	Taj Hotels	CUST-ZP1H7DR	2022-03-03T10:05:22Z	1200	INR	MERCH-023	Travel	ACH	Mumbai	MH	IND	Frequent Traveler
TXN-ZK1S8M5T	Purchase	0.07	false	Amazon Go	CUST-LP2A8JV	2024-04-01T13:55:10Z	312.85	CAD	MERCH-012	Retail	Debit Card	Toronto	ON	CAN	Saver
TXN-HS2J4B6K	Purchase	0.08	false	Cinepolis	CUST-VD5H9MT	2023-08-14T18:34:24Z	54.2	GBP	MERCH-015	Entertainment	Mobile Payment	London	LDN	GBR	Online Shopper
TXN-QX8L1S6V	Purchase	0.06	false	Whole Foods Market	CUST-PT4B6RT	2021-05-09T09:24:38Z	250	USD	MERCH-016	Grocery	Debit Card	San Jose	CA	USA	Saver
TXN-9K3V7N2Z	Purchase	0.39	false	Global Luxury Cars	CUST-FD1V3ZF	2022-08-03T10:30:00Z	6298	AED	MERCH-024	Retail	Credit Card	Dubai	DXB	ARE	High Spender
TXN-2P8D6J1M	Transfer	0.71	true	Global Luxury Cars	CUST-FD1V3ZF	2022-08-03T10:51:00Z	7999	AED	MERCH-024	Retail	ACH	Dubai	DXB	ARE	High Spender
TXN-GN6Z1K8V	Fee	0.01	false	Cinepolis	CUST-CT3D1PX	2021-02-23T08:14:07Z	2.5	MXN	MERCH-018	Entertainment	ACH	Mexico City	CMX	MEX	Online Shopper
TXN-PQ2R7D3K	Purchase	0.37	false	Hilton Hotels	CUST-XV7M2LK	2023-11-04T14:57:18Z	1595	USD	MERCH-025	Travel	Credit Card	New York	NY	USA	Frequent Traveler
TXN-ZV4P1B7J	Purchase	0.07	false	Shanghai Luxury	CUST-BW6K9JF	2022-05-21T17:05:45Z	34	CNY	MERCH-026	Other	Debit Card	Shanghai	SH	CHN	Occasional User
TXN-5B1L6N7V	Refund	0.05	false	Netflix	CUST-QW7Z2CV	2022-12-23T21:00:29Z	79.99	USD	MERCH-017	Entertainment	Credit Card	Los Angeles	CA	USA	Online Shopper
TXN-BR8K2X6F	Purchase	0.18	false	Nordstrom	CUST-RF8Z72X	2024-03-19T12:05:00Z	1420	USD	MERCH-027	Retail	Credit Card	Chicago	IL	USA	High Spender
TXN-6F3B9J2L	Purchase	0.02	false	Cinepolis	CUST-VD5H9MT	2021-07-26T15:30:00Z	22	MXN	MERCH-018	Entertainment	Mobile Payment	Mexico City	CMX	MEX	Online Shopper

What the 200 rows show

from the 200-row sample

Transfer (transaction type) stands out: 23 of its 23 rows have is_fraud_suspected = true, against 13 of 177 for the rest.

18%is_fraud_suspected = true
0.08median risk_score
6payment methods
6customer segments
7merchant categories
10currencies

Is fraud suspected rate by transaction_typeis_fraud_suspected = true

risk_score200 rows, in bands of 0.1

Median 0.08, from 0.0 to 0.94.

merchant_name198 rows with a value · 2 left blank

Tim Hortons29
Netflix18
Whole Foods Market16
Amazon.com14
Starbucks Japan13
Cinepolis13
Radisson Blu11
Amazon Go11
Apple Store10
Global Luxury Cars10

16 columns by typefrom the column list below

string 12
float 2
datetime 1
boolean 1

Columns

16 columns in four groups

blueprint · 16 columns
column	type	description	example
Text 12 columns
`transaction_id`	string	Unique identifier for each financial transactionunique	TXN-9D2M1BJC
`customer_id`	string	Unique identifier for the customer involved in the transaction	CUST-RF8Z72X
`currency`	string	ISO currency code used for the transaction (e.g., USD, EUR)10 currencies	USD
`merchant_id`	string	Unique identifier for the merchant or vendor	MERCH-001
`merchant_name`	string	Name of the merchant or vendoroptional	Apple Store
`merchant_category`	string	Category of the merchant (e.g., Grocery, Dining, Travel)8 values	Retail
`transaction_type`	string	Type of transaction (e.g., Purchase, Withdrawal, Transfer, Payment)7 values	Purchase
`payment_method`	string	Payment method used (e.g., Credit Card, Debit Card, ACH, Cash)7 values	Credit Card
`location_city`	string	City where the transaction took placeoptional	San Francisco
`location_state`	string	State or province where the transaction took placeoptional	CA
`location_country`	string	Country where the transaction took place10 countries · optional	USA
`customer_segment`	string	Assigned customer profile segment based on spending habits (e.g., High Spender, Saver, Occasional User)6 values · optional	High Spender
Numbers 2 columns
`transaction_amount`	float	Monetary value of the transaction0 or more	11245.85
`risk_score`	float	Calculated risk score for the transaction or customer (0-1 scale)0 to 1 · optional	0.26
Dates and times 1 column
`transaction_date`	datetime	Date and time when the transaction occurred	2022-11-15T14:22:32Z
True or false 1 column
`is_fraud_suspected`	boolean	Flag indicating whether the transaction is suspected to be fraudulentoptional	false

Use it for

A finance dashboard
The is_fraud_suspected rate, risk_score by transaction_type and a breakdown of merchant_name. Excel, Power BI or Tableau.
Why do 36 of 200 rows have is_fraud_suspected = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Transactions200
TXN-9D2M1BJC0.26Purchase
TXN-GH5F2K8N0.73Purchase
TXN-VT4N8Q2Y0.62Transfer
A software demo
Believable transactions with customer_id, transaction_date and transaction_amount to fill a screen in front of a buyer.

This dataset200 rows16 columns

Yours1,000 rows16 columnslocation_city: UK only

blueprint · financial-transaction-pattern-analysis

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

Group transactions by account type
Identify recurring payment intervals
Calculate average transaction value
Detect outlier transactions

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: financial-transaction-pattern-analysis

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?