AML is triggered for the applicants that are currently approved. If you approve a rejected applicant manually, you need to initiate a new AML check using this API method. If the applicant was approved by the system, the AML check will be performed as described below.

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
Adverse Media General	General AML/CFT	Bribery Graft Kickbacks Political corruption Organized crime Criminal association Racketeering Smuggling (excl. drugs, money, people or guns/weapons/arms) Poaching Illegal logging Animal cruelty Corruption Other environmental crime People smuggling Migrant smuggling Smuggling of animals Illegal pollution/waste GMigrant trafficking Sanctions violation/risk/connection Tax-related smuggling
Adverse Media Financial Crime	Financial AML/CFT	Money laundering Insider trading Tax-related offenses FTax evasion Market manipulation
	Financial Difficulty	Bankruptcy Financial difficulty Unexpected resignation of CFO Layoff /retrenchments/furloughs Store closures Late payment to creditors
	Other Financial	Loan sharking Usury Predatory lending Questionable billing practices Non-specific financial crime Tax avoidance (not illegal) Creative accounting
Adverse Media Narcotics	Narcotics AML/CFT	Drug trafficking Drug distribution Drug production Drug dealing
Adverse Media Fraud	Fraud-linked	Fraud Scams Swindles Counterfeiting Forgery Identity theft Impersonation Mortgage fraud Identity fraud Corporate fraud Electronic fraud (E-fraud) Misrepresentation VAT fraud Counterfeiting currency Residential fraud Non-specified fraud
Adverse Media Violent crime	Violence AML/CFT	Kidnapping Abduction Held against the will Murder (Committed, planned, or attempted) Child pornography People trafficking Organ trafficking Arms trafficking Homicide Assassination Coercion Child sexual abuse Pimping Sex trafficking Human trafficking Slavery Sexual exploitation (incl. of children) Grievous bodily harm/injury Illegal restraint Hostage-taking Extortion Robbery Child trafficking Illegal possession of guns, weapons, or explosives Illegal sale of guns, weapons, or explosives
Adverse Media Violent crime	Adverse Media Violence NON AML/CFT	Rape Sexual abuse Pedophilia Assault Battery Domestic abuse Elder child abuse Physical abuse Domestic violence Molestation Sexual harassment Purposeful transmission of HIV Human rights abuses by state and non-state actors Genocide War crimes Massacres Torture Illegal pornography Manslaughter Other violent crime Other sexual crime
Adverse Media Terrorism	Terrorism	Terrorist-related Member of a terrorist organization Terrorist attack Terror/terrorist financing Aiding of terrorists/terrorism Cyberterrorism Political terrorism Terroristic oppression
Adverse Media Terrorism	Cybercrime	Computer-related cybercrime Illegal access to information systems Illegal system interference Illegal data interference Illegal interception
Other crimes	Adverse Media Other Serious	Perjury Obstruction of Justice False filings False statements Fugitive Election interference Escaped from imprisonment Arson Spying Espionage OTreason Civil disorder
	Adverse Media Other Minor	Possession of drugs Possession of drug paraphernalia Illegal prostitution Misconduct Vandalism False advertising Drug possession Labour abuse DUI (Driving under the influence) Health & safety issue Human resource issue/dispute Litigation Security related issue Minor-sounding, difficult-to-classify offences Non-specific crime Conspiracy for a non-specific crime
	Adverse Media Regulatory	Antitrust Price fixing Illegal gambling Forfeiture Denied entity Anti-competitive issues Product dumping Regulatory issues Regulatory breach Greenmail Churning Regulatory fines Collusion Industry sanctions/fine /discipline Securities violations