Data extraction

There are two types of watchlist screening:

Information-based. Textual data, such as the applicant name and date of birth received from the applicant is matched against a comprehensive set of watchlists in order to find/exclude a match.
Document-based. The name and date of birth are extracted from the identity document that was uploaded by the applicant.

In both cases, fuzzy logic is involved to ensure that no spelling variations of the applicant’s name are overlooked.

Watchlist screening

The watchlist screening helps find applicants that are:

Known or suspected terrorists
Sanctioned persons
Politically exposed persons (PEPs)
Persons with criminal background
Persons mentioned in adverse media

The potentially matching data from various sanctions and watchlists is compared against the applicant profile to establish a match. Below you will find a list of matches available in Sumsub.

Match type	Description
True positive	Applicant data exactly matches one or several watchlist records. Full DOB of the applicant along with their name matches one or several watchlist records, but middle name omitted/abbreviated.
Potential match	Name of the applicant matches one or several watchlist records, but the age/year of birth data is missing or the applicant is suspected of committing a crime. When two of three words in the applicant name match one or several watchlist records, along with their year of birth or age.
False positive	Applicant data doesn't match the source, or it matches, but the context clearly shows the person doesn't pose any threat.
Unknown	Applicant name in the document contains only one word. There can also be cases with the matching applicant common name and abundance of matches. In such tricky cases the Sumsub client makes the final decision regarding the applicant.

Match processing

Applicants with False positive statuses are automatically approved; all the rest are either declined or assigned the Requires action status and then delegated to the client compliance officers for further processing.

Here are some examples:

Case	Status	Next action
The applicant name is not found in any of the sources.	N/A	The applicant is automatically approved.
Applicant data exactly matches in one or several watchlist records.	True positive	The applicant is either declined or assigned the Requires action status and then delegated to the client compliance officers for further processing. The action depends on the customer settings.
Applicant data matches the watchlist record, but the record does not include criminal or suspicious information about the person. For example, if the applicant is a crime journalist, and their name features on the article as an author.	False positive	The applicant is assigned the Approved status.
Applicant data matches the watchlist record exactly, but additional information clearly shows that the applicant and the person on the record are different people.	False positive	The applicant is assigned the Approved status.
Name of the applicant matches one or several watchlist records, but the age/year of birth data is missing, or the applicant is suspected of committing a crime.	Potential match	The applicant is assigned the Declined status or Requires action status and then delegated to the client compliance officers for further processing.
The applicant name contains just one word. There can also be cases with the matching applicant common name and abundance of matches.	Unknown	The applicant is assigned the Declined status or Requires action status and then delegated to the client compliance officers for further processing.

Examples

False positive. The applicant name match one or several watchlist records, but their DOB/age is different.
True positive. The applicant name partially matches one or several watchlist records and DOB/age matches these too, but the middle name is abbreviated.
Potential match. Two of three words in the applicant full name match one or several watchlist records, and their DOB/age matches these too.

If applicants are declined based on a watchlist match, they are assigned a respective rejection tag depending on the source where the match was found:

Sanctions
PEP
Criminal records
Adverse Media
On demand, the system can be set to delegate borderline cases to the client compliance officers for manual processing.

Adverse Media categories

General tags	Subcategories	FATF & EU AML Directive 22 predicate offences and other categories
Adverse Media General	Adverse Media General AML/CFT	1. Participation in organised criminal group and racketeering.
		8. Corruption.
		12. Environmental crime.
		16. Smuggling, Migrant smuggling.
-	Adverse Media Cybercrime	22. Cybercrime.
Adverse Media Terrorism	Adverse Media Terrorism	2. Terrorism.
Adverse Media Violent crime	Adverse Media Violence AML/CFT	3. Trafficking in human being and migrants.
		4. Sexual exploitation.
		6. Illicit arms trafficking.
		14. Kidnapping, illegal restraint and hostage taking.
		18. Extortion.
		13. Murder, grievous bodily harm.
	Adverse Media Violence NON AML/CFT	Other crime.
	Adverse Media Sexual Crime	Other sexual crime.
Adverse Media Narcotics	Adverse Media Narcotics AML/CFT	5. Illicit trafficking in narcotics drugs and psychotropic substances.
Adverse Media Fraud	Adverse Media Fraud	9. Fraud.
		10. Counterfeiting of currency.
		11. Counterfeiting of piracy products.
		19. Forgery.
Adverse Media Financial Crime	Adverse Media Financial AML/CFT	21. Insider trading and market manipulation.
	Adverse Media Financial AML/CFT	17. Tax crimes relating to direct and indirect taxes, as laid down by national law.
	Adverse Media Financial Difficulty	Other Financial Difficulty related crimes from "Financial Crime" category.
	Adverse Media Other Financial	Other Financial crimes from "Financial Crime" category.
Other crimes	Adverse Media Property	7. Illicit trafficking in stolen goods and other goods.
		15. Robbery and theft.
		20. Piracy.
	Adverse Media Other Serious	Other serious crimes from "Other crimes" category.
	Adverse Media Other Minor	Other minor crimes from "Other crimes" category.
	Adverse Media Regulatory	Topics that relate to activity that contravenes regulation - often this is specific to a particular country or state or industry. Includes Fines or disciplinary action by regulators and industry bodies; collusion / antitrust / price-fixing issues; specific entities that have been denied by industry bodies and regulators; specific industry related issues such as front-running, greenmail, and churning for the stock market, or illegal gambling. Note: This category does not include any of the predicate offences, however, we recommend selecting it for screening.