Finance
21 columns
201 rows
8 formats

Online Merchant Fraud Indicators

This dataset provides merchant-level fraud signals and outcomes, including transaction volumes, chargeback rates, fraud losses, and calculated risk scores. Designed for supervised learning and analytics, it enables robust fraud detection, risk profiling, and compliance monitoring for online payment platforms. The comprehensive structure supports both operational and predictive use cases in fraud prevention.

last updated 22 Jul 2025
by GoMask

The brief that made it

Supervised machine learning for merchant fraud detection

Sample rows

preview · 8 of 201 rows · all 21 columns

merchant_idstring	fraud_type_last_30dstring	risk_scorefloat	fraud_flag_last_30dboolean	merchant_categorystring	merchant_namestring	merchant_emailstring	merchant_phonestring	merchant_registration_datedate	merchant_countrystring	website_urlstring	num_transactions_last_30dinteger	total_transaction_amount_last_30dfloat	avg_transaction_amount_last_30dfloat	chargeback_rate_last_30dfloat	dispute_rate_last_30dfloat	num_fraud_transactions_last_30dinteger	fraud_loss_amount_last_30dfloat	account_statusstring	last_account_review_datedate	risk_levelstring
M001	none	0.12	false	electronics	Vivid Electronics Ltd	sales@vividelec.com	+1-7263481290	2021-07-14	US	https://vividelec.com	2321	108900	46.9	0.007	0.011	0	0	active	2024-05-24	low
M002	none	0.22	false	arts_and_crafts	The Artisans Studio	contact@artisansstudio.net	+46-839201274	2023-11-30	SE	https://artisansstudio.net	11	158	14.36	0	0.02	0	0	active	2024-04-15	medium
M003	none	0.13	false	food_delivery	Zippy Food Delivery	info@zippyfood.io	+65-90981822	2022-03-18	SG	https://zippyfood.io	1954	38625.5	19.76	0.008	0.012	0	0	active	2024-06-05	low
M004	none	0.28	false	luxury_goods	Luxora Fine Goods	luxora@luxgoods.fr	+33-156882910	2021-10-21	FR	https://luxgoods.fr	5	8200	1640	0.04	0.03	0	0	active	2024-05-18	medium
M005	none	0.15	false	auto_parts	QuickAuto Parts	help@quickauto.ca	+1-6473819021	2023-06-09	CA	https://quickauto.ca	799	31960.4	39.99	0.013	0.014	0	0	active	2024-05-22	low
M006	none	0.26	false	books	Bookwise Marketplace	blank	+44-2081234567	2022-12-12	UK	https://bookwise.co.uk	43	980	22.79	0.02	0.03	0	0	active	blank	medium
M007	none	0.1	false	grocery	FreshMart Grocery	support@freshmart.de	+49-3024111100	2023-03-21	DE	https://freshmart.de	1208	18120	15.01	0.009	0.01	0	0	active	2024-05-30	low
M008	none	0.18	false	photography	The Photoworks Hub	hello@photoworkshub.se	+46-739210648	2024-01-13	SE	https://photoworkshub.se	6	92	15.33	0	0	0	0	active	blank	low
M009	stolen_card	0.71	true	electronics_resale	AutoTech Resale	info@autotechresale.com	blank	2022-08-20	US	https://autotechresale.com	118	39584	335.46	0.088	0.095	7	3470	under_review	2024-05-26	high
M010	none	0.14	false	food_delivery	Gourmet Express UK	gourmet@gourexpress.uk	+44-2078214564	2023-02-18	UK	https://gourexpress.uk	1678	40272	24	0.012	0.015	0	0	active	2024-05-29	low
M011	none	0.23	false	arts_and_crafts	NZ Crafty Hands	hello@craftyhands.nz	blank	2023-12-04	NZ	blank	7	40	5.71	0	0	0	0	active	blank	medium
M012	none	0.19	false	books	Urban Reads	contact@urbanreads.com	+1-9082219987	2021-06-15	US	https://urbanreads.com	69	1572	22.78	0.019	0.018	0	0	active	2024-04-11	low
M013	friendly_fraud	0.85	true	home_furnishings	Chic Home Styles	info@chichomestyles.ch	+41-223910002	2021-11-17	CH	https://chichomestyles.ch	9	8750	972.22	0.06	0.07	3	1620	under_review	2024-05-20	critical
M014	none	0.16	false	subscriptions	Snap Subscriptions	support@snapsubs.com	+49-3029301821	2023-05-12	DE	https://snapsubs.com	1382	20216	14.63	0.011	0.013	0	0	active	2024-06-04	low
M015	none	0.11	false	electronics	Euro Electronics	euro@elecde.de	blank	2022-09-10	DE	https://elecde.de	2017	96815	48	0.007	0.012	0	0	active	2024-05-25	low
M016	other	0.94	true	alcohol	Pioneer Alcohols	sales@pioneeralco.com	+44-2081923746	2024-02-09	UK	https://pioneeralco.com	58	2045	35.26	0.25	0.15	5	1420	suspended	2024-05-30	critical
M017	none	0.26	false	grocery	Aussie MarketPlace	blank	+61-398283183	2024-04-01	AU	https://aussiemarket.au	18	310	17.22	0.07	0.08	0	0	under_review	2024-06-04	medium
M018	none	0.13	false	auto_parts	Onyx Auto Spares	contact@onyxautosp.co.uk	+44-2078821930	2022-10-08	UK	https://onyxautosp.co.uk	432	16312	37.77	0.011	0.014	0	0	active	2024-05-18	low
M019	none	0.18	false	food_delivery	Sweet Treats AU	hello@sweettreats.au	+61-287392832	2024-01-29	AU	https://sweettreats.au	21	410	19.52	0	0	0	0	active	blank	low
M020	none	0.32	false	home_furnishings	Sculpted Living	info@sculptedliving.se	blank	2022-05-16	SE	https://sculptedliving.se	12	4720	393.33	0.02	0.02	0	0	active	2024-05-10	medium

What the 201 rows show

from the 201-row sample

None (fraud type last 30d) stands out: mean risk_score is 0.20, against 0.81 for the rest.

13%fraud_flag_last_30d = true
0.22median risk_score
4account statuses
4risk levels
12merchant countries
35median num_transactions_last_30d

Mean risk_score by fraud_type_last_30d201 rows

risk_score201 rows, in bands of 0.1

Every row with risk_score of 0.6 or more has fraud_flag_last_30d = true. None below it.

merchant_category201 rows · top 10 of 16 values

books36
grocery24
electronics21
home_furnishings19
arts_and_crafts15
food_delivery14
subscriptions13
auto_parts12
alcohol12
luxury_goods11

21 columns by typefrom the column list below

string 10
integer 2
float 6
date 2
boolean 1

Columns

21 columns in four groups

blueprint · 21 columns
column	type	description	example
Text 10 columns
`merchant_id`	string	Unique identifier for the merchant.unique	M001
`merchant_name`	string	Registered name of the merchant.	Vivid Electronics Ltd
`merchant_email`	string	Primary contact email address for the merchant.optional	sales@vividelec.com
`merchant_phone`	string	Primary contact phone number for the merchant.optional	+1-7263481290
`merchant_country`	string	Country where the merchant is registered.12 countries	US
`merchant_category`	string	Business category or industry of the merchant (e.g., electronics, apparel, etc.).	electronics
`website_url`	string	Official website URL of the merchant.optional	https://vividelec.com
`fraud_type_last_30d`	string	Type of fraud detected for the merchant in the last 30 days (if any).5 values · optional	none
`account_status`	string	Current status of the merchant account.active · suspended · closed · under_review	active
`risk_level`	string	Categorical risk level for the merchant.low · medium · high · critical	low
Numbers 8 columns
`num_transactions_last_30d`	integer	Total number of transactions processed by the merchant in the last 30 days.0 or more	2321
`total_transaction_amount_last_30d`	float	Total value of transactions processed by the merchant in the last 30 days (in platform currency).0 or more	108900
`avg_transaction_amount_last_30d`	float	Average transaction value for the merchant in the last 30 days.0 or more	46.9
`chargeback_rate_last_30d`	float	Proportion of transactions resulting in chargebacks over the last 30 days (0-1).0 to 1	0.007
`dispute_rate_last_30d`	float	Proportion of transactions resulting in disputes over the last 30 days (0-1).0 to 1	0.011
`num_fraud_transactions_last_30d`	integer	Number of transactions identified as fraudulent in the last 30 days.0 or more	0
`fraud_loss_amount_last_30d`	float	Total monetary loss due to fraud for the merchant in the last 30 days.0 or more	0
`risk_score`	float	Calculated risk score for the merchant (0-1, where 1 is highest risk).0 to 1	0.12
Dates and times 2 columns
`merchant_registration_date`	date	Date the merchant registered on the platform.	2021-07-14
`last_account_review_date`	date	Date of the most recent manual or automated account review.optional	2024-05-24
True or false 1 column
`fraud_flag_last_30d`	boolean	Indicates if the merchant was flagged for fraud in the last 30 days.	false

Use it for

A finance dashboard
The fraud_flag_last_30d rate, risk_score by fraud_type_last_30d and a breakdown of merchant_category. Excel, Power BI or Tableau.
Why do the 175 none rows have a mean risk_score of 0.20?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Merchants201
M0010.12none
M0090.71stolen_c…
M0130.85friendly…
A software demo
Believable merchants with merchant_name, merchant_email and merchant_phone to fill a screen in front of a buyer.

This dataset201 rows21 columns

Yours1,000 rows21 columnsmerchant_country: UK only

blueprint · online-merchant-fraud-indicators

Behind this dataset

Same schema. As many rows as you need.

These 201 rows came out of a blueprint — 21 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

Each merchant record includes type, country, and signup date.
Fraud likelihood scored by prior events.
Volume and chargeback statistics included.
Flagged accounts marked for review.

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: online-merchant-fraud-indicators

What should your data show?

No signup. No card.

Sample rows

What the 201 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?