Anti-Money Laundering (AML) Checks

Introduction

This page provides an overview of the importance of AML checks and their critical role in preventing and detecting illicit financial activities. It will outline the key areas of focus and the types of checks performed to comply with regulatory requirements.

AML Screening Lists

This subsection details different types of lists used to conduct AML checks, emphasizing their relevance and sources.

• Sanctions
  Leverages thousands of sources that contribute to our comprehensive global coverage, including:
  • America’s Office of Foreign Assets Control (OFAC)
  • United Nations
  • UK’s His Majesty’s Treasury (HMT)
  • European Union
  • Australia’s Department of Foreign Affairs and Trade (DFAT), and many more.
    List from around the world, in 14 different languages are covered.
• Politically Exposed Persons (PEP)
  Some PEPs pose a greater AML risk than others. With that in mind, the levels of PEP risk may be organized into the following levels:
  • High Risk (Level 1): Ruling Royal Families, heads of state/government, national/federal members of government and parliament, top officials in state agencies, military, judiciary, central banks, and political parties.
  • Medium Risk (Level 2): Regional government members, senior officials in international organizations, ambassadors, consuls, and high commissioners.
  • Medium Risk (Level 3): Senior management in state-owned businesses, heads of regional agencies and institutions.
  • Low Risk (Level 4): Local mayors, council members, senior executives of local government bodies, and local judges.
• Warnings and Regulatory Enforcement
  Includes lists of individuals, legal entities, and vessels issued by relevant law enforcement or regulatory bodies. These entities are either involved in international law-breaking activities, under investigation in specific jurisdictions, or found guilty of regulatory breaches in their industry. This can indicate significant financial, compliance, or reputational risks.
  This information is essential for determining whether to engage in business relationships with potential customers, counterparties, and suppliers. It is also valuable for making informed decisions about payments, ensuring they are not being made in furtherance of crimes such as money laundering or insider trading.
• Fitness and Probity
  The fitness and probity dataset includes individuals who have committed transgressions but not necessarily offenses severe enough to deny them access to funds or bank accounts. However, these transgressions might prompt a reassessment of potential business relationships. This category contains lists of individuals and legal entities disqualified or restricted from holding certain positions or participating in specific activities, such as publicly-funded contracts, due to regulatory or code of conduct breaches.
• Adverse Media
  Adverse media solution looks at tens of millions of articles from sources, including international news websites, regional and local news sites, government websites, court judgments, and specialist blogs. These sites cover the globe and are processed by our machine learning models in multiple languages.

for more details on all datasets please contact [email protected]

AML Matching

This section explains the techniques used to match individuals datasets mentioned above.

Fuzzy Match:

Is a matching technique that allows for a variation in spelling or small variations in the spelling of a search term and the entities returned in the search results. The fuzziness will allow 1 phonetic typo per each word from the search term. The fuzziness percentage has more to do with the length of the word to activate the fuzzy match.

Setting the interval is entirely dependent on your risk-based approach and how sure you are that the names you input for searching are correct (e.g. if you take the info directly from the customers' IDs, or if they input it themselves - which would be more prone to error).

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Info

Please note that the impact and use of fuzzy match is inversely proportional to the length of the name. As the search term length increases the relative importance of a deviation in spelling will decrease.

Below is the table indicating the minimum word length necessary to activate fuzzy matching for each level of fuzziness, as well as the requisite minimum number of full words that must be matched at each interval.

Exact Match:

An exact match does not permit any phonetic errors when matching all the words in a search term, though it does allow a rearrangement of the words (e.g., 'John Smith' can match with 'Smith John' in an exact match). However, it does not allow the addition of extra words, which means 'John William Smith' would not match with 'John Smith'.

Exact Match vs Fuzzy Match with 0% Fuzziness:

In comparing a 0% fuzziness setting to an exact match, the distinctions are as follows:

• An exact match forbids the inclusion of additional words; for instance, 'Robert Mugabe' would not match with 'Robert Gabriel Mugabe'.
• When fuzziness is set between 10% and 100%, there is leeway for a +/- 1 year variance in the date of birth. Conversely, for an exact match or when there is 0% fuzziness, the birth year must be an exact match.
• Preprocessing is not factored into an exact match, meaning titles or suffixes such as 'Mr.', 'Ms.', 'Dr.', or 'PhD' remain unaltered.

Ongoing Monitoring (OGM)

Introduction

AML monitoring serves as a continuous watch tool that subscribes a user for ongoing monitoring if his/her name appears in any of the previously mentioned lists. There are two methods to enable ongoing monitoring:

• Auto-subscription: All search users will be automatically subscribed to ongoing monitoring.
• Selective subscription: Ongoing monitoring can be enabled from the IDWise portal for selected users based on their risk profiles.

How to Receive AML Monitoring Updates

You can subscribe to the AML Monitoring Update webhook here to receive notifications about any updates for a specific user.