Finance
20 columns
1,000 rows
8 formats

Transaction Monitoring Alerts

This dataset provides comprehensive records of automated transaction monitoring alerts, capturing suspicious activity such as unusual patterns, velocity checks, geographic anomalies, and behavioral red flags. It includes transaction details, customer risk scores, alert types and severities, investigation status, and resolution notes, making it ideal for financial crime detection, compliance reporting, and operational analytics.

last updated 1 Nov 2025
by GoMask

The brief that made it

Automated fraud and financial crime detection

Sample rows

preview · 8 of 1,000 rows · all 20 columns

alert_idstring	alert_severitystring	previous_alert_count_30dinteger	transaction_currencystring	transaction_idstring	customer_idstring	alert_datetimedatetime	transaction_datetimedatetime	transaction_amountfloat	transaction_typestring	alert_typestring	alert_statusstring	triggered_rulestring	transaction_countrystring	transaction_citystring	customer_risk_scorefloat	investigator_idstring	resolution_datetimedatetime	alert_descriptionstring	resolution_notesstring
ALRT-00001	medium	3	USD	TXN-10001	CUST-0001	2024-06-15T09:15:22Z	2024-06-15T09:13:17Z	2550.75	withdrawal	velocity	open	RULE_VEL_345	US	Las Vegas	35.2	blank	blank	Multiple withdrawals detected in short period, exceeding account frequency threshold.	blank
ALRT-00002	low	1	EUR	TXN-10002	CUST-0002	2024-06-01T14:11:03Z	2024-06-01T13:59:57Z	999.99	payment	pattern_anomaly	closed	RULE_PATT_278	FR	Paris	24.7	INV-001	2024-06-02T10:20:40Z	Unusual payment pattern flagged for review, below typical amount for this account.	Alert closed after verifying customer activity matched expected behavior.
ALRT-00003	high	12	GBP	TXN-10003	CUST-0003	2024-05-28T17:23:19Z	2024-05-28T17:22:00Z	7500	transfer	geographic_anomaly	under_review	RULE_GEO_111	GB	London	78.8	INV-002	blank	Transfer initiated from new location in London, inconsistent with customer's previous activity.	blank
ALRT-00004	low	0	USD	TXN-10004	CUST-0004	2024-06-16T10:55:02Z	2024-06-16T10:52:28Z	129.5	deposit	velocity	open	RULE_VEL_419	US	Chicago	9.3	blank	blank	Deposit frequency exceeded standard threshold for low-risk profile.	blank
ALRT-00005	critical	27	CAD	TXN-10005	CUST-0005	2024-06-10T13:45:29Z	2024-06-10T13:44:02Z	84500	transfer	behavioral_red_flag	escalated	RULE_BEHAV_824	CA	Toronto	95.9	INV-003	2024-06-11T09:34:13Z	High-value transfer flagged due to unusual beneficiary and recent high risk score.	Escalated to senior investigator for further analysis due to critical severity.
ALRT-00006	medium	2	AUD	TXN-10006	CUST-0006	2024-05-18T08:12:57Z	2024-05-18T08:11:22Z	420.1	payment	velocity	open	RULE_VEL_279	AU	Sydney	28.2	blank	blank	Rapid sequence of payments detected, flagged for velocity anomaly.	blank
ALRT-00007	critical	34	CNY	TXN-10007	CUST-0007	2024-06-09T21:40:24Z	2024-06-09T21:38:45Z	5000000	deposit	pattern_anomaly	closed	RULE_PATT_512	CN	Shanghai	99.1	INV-004	2024-06-10T14:15:35Z	Large deposit in CNY flagged for pattern anomaly, matches recent high-risk activity.	Closed after confirming funds origin with compliance team, flagged for monitoring.
ALRT-00008	low	0	USD	TXN-10008	CUST-0008	2024-06-13T12:15:21Z	2024-06-13T12:14:01Z	89	withdrawal	other	open	RULE_MISC_105	US	Salem	6.4	blank	blank	Small withdrawal flagged for pattern check based on customer age and activity.	blank
ALRT-00009	high	14	BRL	TXN-10009	CUST-0009	2024-06-11T07:03:33Z	2024-06-11T07:01:10Z	120050.25	transfer	geographic_anomaly	under_review	RULE_GEO_908	BR	Rio de Janeiro	71.4	INV-005	blank	High-value transfer initiated from Rio, location differs from usual transaction pattern.	blank
ALRT-00010	low	0	JPY	TXN-10010	CUST-0010	2024-06-17T15:00:45Z	2024-06-17T14:59:30Z	16.35	deposit	pattern_anomaly	open	RULE_PATT_453	JP	Tokyo	8.7	blank	blank	Micro deposit flagged for pattern anomaly, matches test activity profile.	blank
ALRT-00011	medium	8	SEK	TXN-10011	CUST-0011	2024-06-12T13:12:15Z	2024-06-12T13:11:44Z	4990	withdrawal	geographic_anomaly	under_review	RULE_GEO_211	SE	Stockholm	62.5	INV-006	blank	Withdrawal from Stockholm flagged as geographic anomaly for review.	blank
ALRT-00012	low	1	USD	TXN-10012	CUST-0012	2024-05-20T18:23:49Z	2024-05-20T18:23:21Z	250	payment	velocity	open	RULE_VEL_723	US	San Francisco	13.1	blank	blank	Payment frequency slightly above average, flagged for monitoring.	blank
ALRT-00013	critical	38	AED	TXN-10013	CUST-0013	2024-06-14T05:22:38Z	2024-06-14T05:21:22Z	310000	deposit	behavioral_red_flag	escalated	RULE_BEHAV_991	AE	Dubai	98.5	INV-007	2024-06-14T07:02:10Z	Critical behavioral anomaly detected: deposit from high-risk region in Dubai.	Case escalated for compliance review due to critical risk score and transaction value.
ALRT-00014	medium	2	CAD	TXN-10014	CUST-0014	2024-06-02T20:18:44Z	2024-06-02T20:17:09Z	4100	payment	velocity	open	RULE_VEL_587	CA	Montreal	27.6	blank	blank	Payment frequency flagged for review, matches medium-risk profile.	blank
ALRT-00015	critical	19	GBP	TXN-10015	CUST-0015	2024-06-03T16:11:57Z	2024-06-03T16:10:40Z	1000000	transfer	pattern_anomaly	closed	RULE_PATT_821	GB	Manchester	91.2	INV-008	2024-06-04T09:30:06Z	Large transfer flagged for pattern anomaly, matches prior critical alerts.	Closed after confirming legitimate business transaction, flagged for ongoing monitoring.
ALRT-00016	medium	1	USD	TXN-10016	CUST-0016	2024-06-07T11:20:01Z	2024-06-07T11:19:17Z	175	withdrawal	velocity	open	RULE_VEL_402	US	Dallas	22.9	blank	blank	Withdrawal frequency flagged for monitoring, matches medium-risk behavior.	blank
ALRT-00017	critical	35	CNY	TXN-10017	CUST-0017	2024-06-10T20:10:18Z	2024-06-10T20:09:11Z	6000000	deposit	pattern_anomaly	escalated	RULE_PATT_952	CN	Beijing	97.4	INV-009	2024-06-11T12:55:01Z	Critical anomaly: large deposit in CNY with recent risk escalation.	Escalated for further review due to high risk and transaction value.
ALRT-00018	low	0	USD	TXN-10018	CUST-0018	2024-06-11T12:05:28Z	2024-06-11T12:03:59Z	78.75	withdrawal	velocity	open	RULE_VEL_377	US	New York	5.5	blank	blank	Low amount withdrawal flagged for velocity anomaly check.	blank
ALRT-00019	high	11	BRL	TXN-10019	CUST-0019	2024-05-30T07:41:12Z	2024-05-30T07:40:22Z	17250	transfer	geographic_anomaly	under_review	RULE_GEO_662	BR	Brasilia	65.7	INV-010	blank	Transfer from Brasilia flagged due to location and amount anomaly.	blank
ALRT-00020	medium	2	USD	TXN-10020	CUST-0020	2024-06-16T22:01:58Z	2024-06-16T22:00:47Z	500	payment	other	open	RULE_MISC_542	US	Chicago	19.8	blank	blank	Medium-value payment flagged for monitoring due to frequency increase.	blank

What the 1,000 rows show

from the 1,000-row sample

Critical (alert severity) stands out: mean previous_alert_count_30d is 27.6, against 3.7 for the rest.

2median previous_alert_count_30d
4alert statuses
5transaction types
5alert types
13transaction countries
67transaction cities

Mean previous_alert_count_30d by alert_severity1,000 rows

previous_alert_count_30d1,000 rows, in bands of 5

Median 2, from 0 to 41.

transaction_currency1,000 rows · top 10 of 13 values

USD418
CAD72
EUR65
CNY56
BRL55
JPY55
GBP52
SEK44
INR42
ZAR42

20 columns by typefrom the column list below

string 14
integer 1
float 2
datetime 3

Columns

20 columns in three groups

blueprint · 20 columns
column	type	description	example
Text 14 columns
`alert_id`	string	Unique identifier for each transaction monitoring alertunique	ALRT-00001
`transaction_id`	string	Unique identifier for the transaction associated with the alert	TXN-10001
`customer_id`	string	Unique identifier for the customer involved in the transaction	CUST-0001
`transaction_currency`	string	ISO currency code for the transaction (e.g., USD, EUR)	USD
`transaction_type`	string	Type of transaction (e.g., deposit, withdrawal, transfer, payment)deposit · withdrawal · transfer · payment · other	withdrawal
`alert_type`	string	Category of alert (e.g., velocity, geographic anomaly, behavioral red flag)5 values	velocity
`alert_severity`	string	Severity level of the alertlow · medium · high · critical	medium
`alert_status`	string	Current status of the alert (e.g., open, under_review, closed, escalated)open · under_review · closed · escalated	open
`triggered_rule`	string	Name or identifier of the rule or logic that triggered the alert	RULE_VEL_345
`transaction_country`	string	Country where the transaction originated (ISO country code)	US
`transaction_city`	string	City where the transaction originatedoptional	Las Vegas
`alert_description`	string	Detailed description of the alert and the unusual activity detectedoptional	Multiple withdrawals dete…
`investigator_id`	string	Identifier for the investigator assigned to review the alertoptional	INV-001
`resolution_notes`	string	Notes or comments entered by the investigator regarding the resolution of the alertoptional	Alert closed after verify…
Numbers 3 columns
`transaction_amount`	float	Monetary amount of the transaction0 or more	2550.75
`customer_risk_score`	float	Risk score assigned to the customer at the time of alert (0-100)0 to 100 · optional	35.2
`previous_alert_count_30d`	integer	Number of alerts generated for this customer in the past 30 days0 or more · optional	3
Dates and times 3 columns
`alert_datetime`	datetime	Timestamp when the alert was generated	2024-06-15T09:15:22Z
`transaction_datetime`	datetime	Timestamp of the transaction that triggered the alert	2024-06-15T09:13:17Z
`resolution_datetime`	datetime	Timestamp when the alert was resolved or closedoptional	2024-06-02T10:20:40Z

Use it for

A finance dashboard
Previous_alert_count_30d by alert_severity and a breakdown of transaction_currency. Excel, Power BI or Tableau.
Why do the 158 critical rows have a mean previous_alert_count_30d of 27.6?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Alerts1,000
ALRT-000013medium
ALRT-000021low
ALRT-0000312high
A software demo
Believable alerts with transaction_id, customer_id and alert_datetime to fill a screen in front of a buyer.

This dataset1,000 rows20 columns

Yours1,000 rows20 columnstransaction_country: UK only

blueprint · transaction-monitoring-alerts-2

Behind this dataset

Same schema. As many rows as you need.

These 1,000 rows came out of a blueprint — 20 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

Alert ID unique per scenario trigger
Scenario types: velocity, amount, geography, structuring
Alert priority: low, medium, high, critical
Customer risk profile considered
Historical behavior baseline
Investigation assignment automated
SLA for alert resolution by priority
Disposition: false positive, true positive, escalate to SAR

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: transaction-monitoring-alerts-2

What should your data show?

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Sample rows

What the 1,000 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?