Finance
21 columns
201 rows
8 formats

Credit Card Fraud Alerts

This dataset provides detailed records of credit card fraud alerts, including suspicious transaction details, merchant information, alert status, and investigator actions. It enables financial institutions to detect, investigate, and respond to fraudulent activities efficiently, supporting enterprise risk management and loss mitigation.

last updated 20 Aug 2025
by GoMask

The brief that made it

Real-time fraud detection and alerting

Sample rows

preview · 8 of 201 rows · all 21 columns

alert_idstring	pattern_typestring	previous_similar_alerts_countinteger	merchant_statestring	transaction_idstring	card_number_maskedstring	account_idstring	transaction_datetimedatetime	transaction_amountfloat	currencystring	merchant_namestring	merchant_categorystring	merchant_citystring	merchant_countrystring	transaction_typestring	alert_datetimedatetime	alert_statusstring	alert_priorityinteger	investigator_idstring	suspicion_reasonstring	resolution_notesstring
A4D93F7B0K	amount anomaly	5	TX	TXN96231482	4281	ACCT59322	2024-04-03T15:22:09Z	0.46	USD	SpeedyMart	Convenience Store	Dallas	US	purchase	2024-04-03T15:23:31Z	open	5	blank	Unusually small transaction amount detected at local merchant.	blank
F8K29B1R3Z	location anomaly	1	blank	TXN81345726	1917	ACCT77401	2024-03-28T09:12:15Z	13450.29	JPY	Tokyo Luxury Gifts	Luxury Retail	Tokyo	JP	purchase	2024-03-28T09:13:39Z	investigating	1	RSK0021	Large luxury retail purchase made in foreign country.	Confirmed high-risk, awaiting customer confirmation.
N3J28H4S5Q	high-frequency	4	IL	TXN38542179	5812	ACCT91245	2024-04-12T18:05:53Z	22.13	USD	Quick Cafe	Food & Beverage	Chicago	US	purchase	2024-04-12T18:07:00Z	open	4	blank	High frequency of small purchases within short period.	blank
B7V15G8R2U	amount anomaly	1	CA	TXN97430156	7349	ACCT19385	2024-01-08T11:41:27Z	450	USD	TechSpot	Electronics	San Jose	US	purchase	2024-01-08T11:42:34Z	closed	2	RSK0095	Electronics purchase flagged for amount anomaly.	Verified legitimate after customer contact.
H2S93K6D8W	high-frequency	7	TX	TXN16098234	2047	ACCT65903	2024-04-19T16:28:50Z	6.73	USD	ExpressMart	Convenience Store	Houston	US	purchase	2024-04-19T16:29:51Z	open	5	blank	Repeated microtransactions at convenience store.	blank
M9T72E1F5C	location anomaly	2	blank	TXN43567219	6182	ACCT32018	2024-03-16T13:50:10Z	2450.16	EUR	Parisian Travels	Travel	Paris	FR	purchase	2024-03-16T13:51:56Z	investigating	1	RSK0016	Unusual travel booking in foreign location.	Escalated for international travel verification.
K5F83B6L2Q	type anomaly	2	AZ	TXN67814095	9302	ACCT42873	2024-02-27T17:34:42Z	151.87	USD	Urban Retailers	Retail	Phoenix	US	refund	2024-02-27T17:36:10Z	false_positive	4	RSK0082	Refund transaction flagged for unusual activity.	Refund verified by merchant, no fraud detected.
Q6Z41M9Y7J	amount anomaly	0	blank	TXN31579820	4856	ACCT20594	2024-03-11T11:05:37Z	7400.5	GBP	London Electronics	Electronics	London	GB	purchase	2024-03-11T11:06:53Z	investigating	1	RSK0037	Large electronics purchase in foreign country.	Customer contacted for transaction validation.
T8G92Z4B6L	high-frequency	6	CO	TXN96215348	1096	ACCT79583	2024-02-16T20:31:13Z	9.87	USD	SnackShack	Food & Beverage	Denver	US	purchase	2024-02-16T20:32:30Z	open	5	blank	Unusual frequency of small food purchases detected.	blank
E5M47N2C8J	amount anomaly	1	WA	TXN41275918	3015	ACCT12648	2024-04-06T09:18:44Z	650	USD	BestElectro	Electronics	Seattle	US	purchase	2024-04-06T09:20:02Z	closed	2	RSK0104	Higher-than-usual electronics purchase amount flagged.	Transaction confirmed legitimate by customer.
L2C93H7X4S	high-frequency	3	GA	TXN78135049	7621	ACCT34217	2024-04-13T07:44:29Z	84.37	USD	FreshGrocer	Retail	Atlanta	US	purchase	2024-04-13T07:45:35Z	open	3	blank	Retail purchase flagged due to repeated small transactions.	blank
S1K82W5V6B	amount anomaly	0	blank	TXN63172584	5417	ACCT10127	2024-02-05T15:58:45Z	17652.12	CHF	Geneva Jewelers	Jewelry	Geneva	CH	purchase	2024-02-05T15:59:57Z	investigating	1	RSK0049	High-value jewelry purchase outside usual region.	Pending customer verification for high-value transaction.
G3P48E2F1U	pattern anomaly	1	MI	TXN21349857	8209	ACCT73465	2024-03-20T14:23:07Z	202.43	USD	AutoPartsNow	Automotive	Detroit	US	purchase	2024-03-20T14:24:35Z	open	3	blank	Automotive purchase flagged for repeated use.	blank
W9H26C7M4D	high-frequency	6	FL	TXN98127345	4162	ACCT54812	2024-03-25T08:17:29Z	12	USD	DailyStop	Convenience Store	Miami	US	purchase	2024-03-25T08:18:48Z	closed	4	RSK0077	Microtransaction flagged due to high frequency.	No fraudulent activity detected on review.
C6R82T3P7F	type anomaly	0	blank	TXN52748130	3275	ACCT38621	2024-01-22T10:50:12Z	1832.77	CAD	Toronto TechHub	Electronics	Toronto	CA	transfer	2024-01-22T10:51:31Z	closed	3	RSK0055	Unusual transfer to electronics merchant.	Confirmed transfer with merchant and customer.
U3F61H2Z9P	high-frequency	5	NY	TXN86421935	5784	ACCT24681	2024-04-01T19:33:48Z	15.49	USD	Coffee Express	Food & Beverage	New York	US	purchase	2024-04-01T19:35:11Z	open	4	blank	Small food purchase flagged for repeated location.	blank
V1M34X8Z2G	type anomaly	1	CA	TXN18302754	8201	ACCT57284	2024-02-10T14:59:03Z	110.99	USD	Urban Styles	Retail	Los Angeles	US	withdrawal	2024-02-10T15:00:10Z	false_positive	4	RSK0022	Withdrawal flagged due to unusual activity pattern.	No suspicious activity confirmed on review.
P8L73S6C2B	pattern anomaly	2	CA	TXN50178342	6104	ACCT91837	2024-03-07T16:17:23Z	239.88	USD	ElectroWorld	Electronics	San Diego	US	purchase	2024-03-07T16:18:42Z	closed	3	RSK0111	Electronics purchase flagged for repeated merchant use.	Merchant confirmed no fraudulent activity.
Y8S14M5G7J	high-frequency	3	TX	TXN64831920	7812	ACCT76819	2024-04-08T21:12:37Z	14.24	USD	QuickShop	Convenience Store	Austin	US	purchase	2024-04-08T21:13:46Z	open	5	blank	High frequency of small purchases at same merchant.	blank
R2C87V6B1X	location anomaly	0	blank	TXN17620539	5321	ACCT18462	2024-03-19T12:27:23Z	11500	INR	Mumbai LuxMart	Luxury Retail	Mumbai	IN	purchase	2024-03-19T12:28:44Z	investigating	1	RSK0018	Luxury purchase flagged in foreign location.	Escalated for customer location verification.

What the 201 rows show

from the 201-row sample

High-frequency (pattern type) stands out: mean previous_similar_alerts_count is 5.9, against 1.2 for the rest.

2median previous_similar_alerts_count
4transaction types
4alert statuses
9currencies
9merchant categories
10merchant countries

Mean previous_similar_alerts_count by pattern_type201 rows

previous_similar_alerts_count201 rows, in bands of 1

Median 2, from 0 to 8.

merchant_state159 rows with a value · 42 left blank

TX35
CA23
FL16
CO9
AZ8
WA8
MA8
IL7
MI6
GA5

21 columns by typefrom the column list below

string 16
integer 2
float 1
datetime 2

Columns

21 columns in three groups

blueprint · 21 columns
column	type	description	example
Text 16 columns
`alert_id`	string	Unique identifier for each fraud alertunique	A4D93F7B0K
`transaction_id`	string	Unique identifier for the suspicious transaction	TXN96231482
`card_number_masked`	string	Masked credit card number involved in the transaction (e.g., last 4 digits)	4281
`account_id`	string	Unique identifier for the cardholder's account	ACCT59322
`currency`	string	ISO currency code for the transaction (e.g., USD, EUR)9 currencies	USD
`merchant_name`	string	Name of the merchant where the transaction occurredoptional	SpeedyMart
`merchant_category`	string	Category of the merchant (e.g., Retail, Online Services, Travel)9 categories · optional	Convenience Store
`merchant_city`	string	City where the merchant is locatedoptional	Dallas
`merchant_state`	string	State or province where the merchant is locatedoptional	TX
`merchant_country`	string	Country where the merchant is located10 countries · optional	US
`transaction_type`	string	Type of transaction (e.g., purchase, withdrawal, transfer)purchase · withdrawal · transfer · refund · other	purchase
`alert_status`	string	Current status of the fraud alertopen · investigating · closed · false_positive	open
`suspicion_reason`	string	Short description of why the transaction was flagged as suspicious	Unusually small transacti…
`pattern_type`	string	Type of suspicious pattern detected (e.g., high-frequency, location anomaly, amount anomaly)5 types · optional	amount anomaly
`investigator_id`	string	Identifier for the risk team member assigned to investigate the alertoptional	RSK0021
`resolution_notes`	string	Notes or comments added by the investigator regarding the alert resolutionoptional	Confirmed high-risk, awai…
Numbers 3 columns
`transaction_amount`	float	Amount of the suspicious transaction0 or more	0.46
`alert_priority`	integer	Priority level of the alert (1=highest, 5=lowest)1 to 5	5
`previous_similar_alerts_count`	integer	Number of previous similar alerts for this account/card0 or more · optional	5
Dates and times 2 columns
`transaction_datetime`	datetime	Date and time when the suspicious transaction occurred	2024-04-03T15:22:09Z
`alert_datetime`	datetime	Date and time when the fraud alert was generated	2024-04-03T15:23:31Z

Use it for

A finance dashboard
Previous_similar_alerts_count by pattern_type and a breakdown of merchant_state. Excel, Power BI or Tableau.
Why do the 77 high-frequency rows have a mean previous_similar_alerts_count of 5.9?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Alerts201
A4D93F7B0K5amount a…
F8K29B1R3Z1location…
N3J28H4S5Q4high-fre…
A software demo
Believable alerts with transaction_id, card_number_masked and account_id to fill a screen in front of a buyer.

This dataset201 rows21 columns

Yours1,000 rows21 columnsmerchant_city: UK only

blueprint · credit-card-fraud-alerts

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

Flag mismatched location and spending behavior
Record device used for transaction
Log transaction time and amount
Score alert severity based on historical data
Link transactions to customer profiles

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: credit-card-fraud-alerts

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?