AML Name Screening

The main idea behind this AML measure is to determine whether the person or business has been flagged by a regulatory authority or is otherwise considered high-risk under AML rules. It’s typically performed at onboarding and repeated periodically. That’s because a customer’s risk profile can change. For example, they can become a PEP (family members of PEPs are also considered high-risk, for example). 

Regulators across jurisdictions require financial institutions and other obligated entities to screen customers against relevant lists as part of their Customer Due Diligence (CDD) obligations. This is also part of the risk-based approach to AML and other regulatory requirements, which are interconnected. 

For example, Frameworks such as the FATF’s 40 Recommendations, the EU’s AML Directives, or FinCEN rules all mandate that businesses have processes in place to identify sanctioned individuals or entities before entering into a business relationship with them.

Any organization subject to AML regulations is required to conduct AML name screening. This includes banks, credit unions, fintechs, crypto exchanges, real estate agencies, luxury item/service providers (like private aviation or art auctions), insurance companies, law firms, and other businesses classified as obligated entities under applicable AML legislation. 

The exact scope of screening obligations depends on the jurisdiction and the risk level associated with the customer or their transaction. That’s why automated AML systems are typically used to reduce human error and handle large volumes, flagging potential risks for further investigation. 

When a potential match is returned, the AML software triggers a real-time alert that requires extra manual review by a compliance officer who’s managing the system. 

They then assess whether the match is valid, or if:

• The customer is genuinely the same person as the flagged individual
• It’s a false positive resulting from a common name or data entry discrepancy

If confirmed as a true match, the business needs to follow its internal escalation procedures, which often include mandatory processes like filing a Suspicious Activity Report (SAR). 

Fuzzy matching algorithms calculate the similarity between a submitted name and entries in a database, flagging results that meet a defined threshold of similarity even when the spelling does not match exactly. That’s because names can be spelled differently, transliterated from other scripts, or altered to evade detection on purpose. 

That’s why an efficient AML solution should reduce the risk of false negatives (for example, where a sanctioned individual slips through due to a minor spelling variation).