6 Reasons Your AML Customer Due Diligence Software Might Not Be Sufficient

#1: False Positive Name Matches Must be Filtered Out

As an example, in the instance of due diligence on a Chinese entity or individual,

analysts might run into dozens of variations of a single Mandarin-language name. Open

source research and cross-referencing methodologies must be implemented to ensure the correct name is investigated.

#2: Red Flag Information Must be Cross-Referenced with Primary Sources

To mitigate data errors, human input is required to cross-reference information from

data-aggregation tools and AML software with authoritative primary sources on the open web.

#3: Screening for Politically Exposed Persons is Not Confined

to Database Checks

Given that Politically Exposed Persons (PEP) lists can contain data errors, analysts must conduct social media scrapings to flag self-

reported past or current public service employers.

#4: Words of Estimative Probability Convey Nuance in Analytic Findings

Words of Estimative Probability (WEPs) are an Intelligence Community (IC) practice that

enables analysts to express varying degrees of confidence in Anti-Money Laundering research

findings.Examples:

•Confirmed = 100% Confidence•Highly Likely = 93% Confidence•Likely = 75% Confidence•Possible = 50% Confidence

#5: Regulators are Honing In on Beneficial Ownership Requirements

FinCEN’s Final Rule on CDD requirements means that financial stakeholders must

identify and confirm the beneficial owners of a business entity during the customer

onboarding process. This presents challenges for those using automated transaction

monitoring systems.

#6: Link-Chain Analysis Can Clarify Complicated Business

Relationships

This is another IC methodology, which provides a visual chart of overlapping relationships pertaining to business

interests, transactions, directorships, and address histories.

“6 Reasons Your AML Customer Due Diligence Software Might Not Be Sufficient”

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