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.

Dataset

This subsection details the specific datasets used to conduct AML checks, emphasizing their relevance and sources.

• Sanctions, Warnings and Fitness & Probity
• Politically Exposed Persons (PEP)
• Adverse Media

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).

📘

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.