Finance
18 columns
800 rows
8 formats

Sanctions Screening Results

This dataset provides detailed, real-time and batch sanctions screening results against major global lists such as OFAC, UN, and EU. It includes entity details, match scores, false positive resolutions, and alert dispositions, enabling comprehensive compliance monitoring, audit trails, and risk analytics for financial institutions and regulated organizations.

last updated 1 Nov 2025
by GoMask

The brief that made it

Automated compliance and sanctions screening for onboarding and transactions

Sample rows

preview · 8 of 800 rows · all 18 columns

screening_idstring	alert_dispositionstring	match_scorefloat	is_false_positiveboolean	entity_typestring	screening_datetimedatetime	screening_modestring	entity_idstring	entity_namestring	entity_date_of_birthdate	entity_countrystring	sanctions_liststring	matched_sanctioned_party_idstring	matched_sanctioned_party_namestring	disposition_datetimedatetime	reviewer_idstring	reviewer_namestring	false_positive_resolution_notesstring
SCRN-0001-20240601-001	escalated	0.87	false	individual	2024-06-01T09:05:32Z	real-time	ENT-IND-1001	Maria Gomez	1984-09-16	Mexico	OFAC	OFAC-IND-8702	Maria Gomez	2024-06-01T11:23:44Z	REV-0023	Sarah Lin	blank
SCRN-0002-20240601-002	cleared	0.09	false	company	2024-06-01T09:10:27Z	batch	ENT-CMP-1015	Blue Ocean Trading Ltd.	2007-04-22	United Kingdom	EU	blank	blank	2024-06-01T10:55:12Z	REV-0012	Evan Brooks	blank
SCRN-0003-20240601-003	cleared	0.96	true	individual	2024-06-01T09:15:44Z	real-time	ENT-IND-1002	Mohammed Abbas	1977-11-03	United Arab Emirates	UN	UN-IND-2984	Mohammed Abbas	2024-06-01T11:30:08Z	REV-0019	Alexei Popov	Name matched but date of birth did not align.
SCRN-0004-20240601-004	pending	0.13	false	organization	2024-06-01T09:22:11Z	batch	ENT-ORG-2006	World Health Federation	1992-06-05	France	Other	blank	blank	blank	blank	blank	blank
SCRN-0005-20240601-005	cleared	0.72	true	company	2024-06-01T09:25:07Z	real-time	ENT-CMP-1016	Green Valley Imports	2012-08-11	Canada	OFAC	OFAC-CMP-5581	Green Valley Imports Co.	2024-06-01T11:45:21Z	REV-0021	Priya Singh	Legal entity registered under a different address.
SCRN-0006-20240601-006	escalated	0.81	false	individual	2024-06-01T09:31:44Z	batch	ENT-IND-1003	Jessica Williams	1995-01-24	United States	EU	EU-IND-3342	Jessica Willams	2024-06-01T12:10:19Z	REV-0014	George Carter	blank
SCRN-0007-20240601-007	confirmed	0.93	false	individual	2024-06-01T09:37:53Z	real-time	ENT-IND-1004	Li Wei	1966-04-18	China	UN	UN-IND-1067	Li Wei	2024-06-01T12:25:31Z	REV-0030	Helen Park	blank
SCRN-0008-20240601-008	cleared	0.23	false	company	2024-06-01T09:44:12Z	batch	ENT-CMP-1017	Apex Mining Corp	1998-03-23	Australia	OFAC	blank	blank	2024-06-01T10:59:01Z	REV-0016	Tom Bradley	blank
SCRN-0009-20240601-009	cleared	0.68	true	organization	2024-06-01T09:47:58Z	real-time	ENT-ORG-2007	Global Aid Network	2001-07-30	Kenya	EU	EU-ORG-2044	Global Aid Network	2024-06-01T11:20:15Z	REV-0026	Linda Shaw	Similar name but not active in risk region.
SCRN-0010-20240601-010	cleared	0.14	false	individual	2024-06-01T09:51:41Z	batch	ENT-IND-1005	Elena Ivanova	1981-02-17	Russia	UN	blank	blank	2024-06-01T12:02:18Z	REV-0033	Viktor Sokolov	blank
SCRN-0011-20240601-011	pending	0.06	false	individual	2024-06-01T09:56:24Z	real-time	ENT-IND-1006	Aisha Hassan	1990-12-09	Egypt	Other	blank	blank	blank	blank	blank	blank
SCRN-0012-20240601-012	confirmed	0.54	false	company	2024-06-01T10:01:13Z	batch	ENT-CMP-1018	Sunrise Logistics	2015-11-19	South Africa	OFAC	OFAC-CMP-5593	Sunrise Logistics Ltd.	2024-06-01T12:15:34Z	REV-0028	Thabo Nkosi	blank
SCRN-0013-20240601-013	cleared	0.08	false	individual	2024-06-01T10:07:36Z	real-time	ENT-IND-1007	Clara Johnson	1975-07-26	United States	EU	blank	blank	2024-06-01T10:45:24Z	REV-0013	Robert James	blank
SCRN-0014-20240601-014	cleared	0.69	true	organization	2024-06-01T10:11:19Z	batch	ENT-ORG-2008	FutureTech Foundation	2013-05-14	Germany	UN	UN-ORG-1171	FutureTech Foundation	2024-06-01T11:35:51Z	REV-0031	Klaus Becker	Organization is not related to sanctioned activities.
SCRN-0015-20240601-015	cleared	0.17	false	company	2024-06-01T10:15:44Z	real-time	ENT-CMP-1019	Alpine Ventures AG	2002-02-28	Switzerland	EU	blank	blank	2024-06-01T10:57:56Z	REV-0027	Lars Keller	blank
SCRN-0016-20240601-016	pending	0.11	false	individual	2024-06-01T10:19:28Z	batch	ENT-IND-1008	Vivian Chang	1989-05-02	Singapore	EU	blank	blank	blank	blank	blank	blank
SCRN-0017-20240601-017	escalated	0.76	false	company	2024-06-01T10:22:44Z	real-time	ENT-CMP-1020	Red River Holdings	2009-10-19	United States	OFAC	OFAC-CMP-5632	Red River Holdings LLC	2024-06-01T12:05:51Z	REV-0017	Jessica Chen	blank
SCRN-0018-20240601-018	confirmed	0.84	false	individual	2024-06-01T10:28:11Z	batch	ENT-IND-1009	Sergei Petrov	1988-08-12	Ukraine	UN	UN-IND-3228	Sergei Petrov	2024-06-01T12:18:39Z	REV-0029	Oksana Melnyk	blank
SCRN-0019-20240601-019	cleared	0.21	false	organization	2024-06-01T10:33:25Z	real-time	ENT-ORG-2009	Education for All	2009-09-13	India	Other	blank	blank	2024-06-01T11:40:02Z	REV-0020	Rajesh Kumar	blank
SCRN-0020-20240601-020	pending	0.05	false	company	2024-06-01T10:37:59Z	batch	ENT-CMP-1021	Silver Arrow Shipping	2019-12-01	Brazil	EU	blank	blank	blank	blank	blank	blank

What the 800 rows show

from the 800-row sample

Confirmed (alert disposition) stands out: mean match_score is 0.78, against 0.32 for the rest.

22%is_false_positive = true
0.21median match_score
2screening modes
4sanctions lists
76entity countries

Mean match_score by alert_disposition800 rows

match_score800 rows, in bands of 0.1

Median 0.21, from 0.02 to 0.99.

entity_type800 rows · 4 values

individual342
company249
organization192
other17

18 columns by typefrom the column list below

string 13
float 1
date 1
datetime 2
boolean 1

Columns

18 columns in four groups

blueprint · 18 columns
column	type	description	example
Text 13 columns
`screening_id`	string	Unique identifier for each sanctions screening eventunique	SCRN-0001-20240601-001
`screening_mode`	string	Indicates whether the screening was performed in real-time or batch modereal-time · batch	real-time
`entity_type`	string	Type of entity being screened (e.g., individual, company)individual · company · organization · other	individual
`entity_id`	string	Unique identifier for the entity being screened (internal reference)	ENT-IND-1001
`entity_name`	string	Full name of the entity being screened	Maria Gomez
`entity_country`	string	Country of residence or registration for the entityoptional	Mexico
`sanctions_list`	string	Name of the sanctions list used for screening (e.g., OFAC, UN, EU)OFAC · UN · EU · Other	OFAC
`matched_sanctioned_party_id`	string	Unique identifier of the matched sanctioned party from the list (if any)optional	OFAC-IND-8702
`matched_sanctioned_party_name`	string	Name of the matched sanctioned party (if any)optional	Maria Gomez
`false_positive_resolution_notes`	string	Notes or comments regarding the resolution of a false positive matchoptional	No shared ownership.
`alert_disposition`	string	Final disposition of the alert (e.g., cleared, escalated, confirmed, pending)cleared · escalated · confirmed · pending	escalated
`reviewer_id`	string	Identifier for the reviewer who made the final disposition decisionoptional	REV-0023
`reviewer_name`	string	Name of the reviewer who made the final disposition decisionoptional	Sarah Lin
Numbers 1 column
`match_score`	float	Score indicating the strength of the match between the entity and the sanctioned party (0-1)0 to 1	0.87
Dates and times 3 columns
`screening_datetime`	datetime	Timestamp when the screening was performed	2024-06-01T09:05:32Z
`entity_date_of_birth`	date	Date of birth of the individual or founding date of the company (if applicable)optional	1984-09-16
`disposition_datetime`	datetime	Timestamp when the alert disposition was finalizedoptional	2024-06-01T11:23:44Z
True or false 1 column
`is_false_positive`	boolean	Indicates whether the match was determined to be a false positive	false

Use it for

A finance dashboard
The is_false_positive rate, match_score by alert_disposition and a breakdown of entity_type. Excel, Power BI or Tableau.
Why do the 76 confirmed rows have a mean match_score of 0.78?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Screenings800
SCRN-0001-20240601-0010.87escalated
SCRN-0003-20240601-0030.96cleared
SCRN-0005-20240601-0050.72cleared
A software demo
Believable screenings with screening_datetime, screening_mode and entity_type to fill a screen in front of a buyer.

This dataset800 rows18 columns

Yours1,000 rows18 columnsentity_country: UK only

blueprint · sanctions-screening-results-2

Behind this dataset

Same schema. As many rows as you need.

These 800 rows came out of a blueprint — 18 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

Screening performed at transaction time
Multiple watchlists checked: OFAC SDN, UN, EU, HMT
Match score threshold for alerts
Name matching algorithm: fuzzy, phonetic
False positive rate tracked
True match escalation to compliance
Transaction blocked if positive match
Resolution documented with evidence

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: sanctions-screening-results-2

What should your data show?

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Sample rows

What the 800 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?