Blog > Understanding AML Name Screening: Processes and Best Practices

17th June 2024

Understanding AML Name Screening: Processes and Best Practices

Name screening is fundamental to anti-money laundering, enabling firms to more accurately capture the level of financial compliance risk that individual customers present, and then deploy appropriate mitigation measures.  

Often a regulatory requirement, AML name screening is critical in the fight against financial crime but typically involves the collection and analysis of vast amounts of  structured and unstructured data, and the accurate matching of that information to specific individuals. In contexts where firms struggle to meet those obligations or to manage the screening data burden, automation often provides an advantage – if integrated effectively. 

Given the critical role it plays in combating money laundering, firms must understand how to implement effective name screening – and how to optimise their screening tools as part of a wider compliance solution.  

What is AML Name Screening?

AML name screening is the process of searching customer names for their designation on official sanctions lists, PEP lists and watchlists, or in negative news (adverse media) stories, in order to accurately gauge the level of money laundering risk that they present. 

When firms find customer names designated on relevant sanctions or watchlists, or in negative news media, that information should generate an alert, inform the customer’s risk profile, and ultimately help the compliance team take appropriate action. This may include declining their use of services, freezing transactions or forwarding information to the authorities. 

Firms may take different approaches to AML name screening: 

Manual Screening

A manual name screening process involves manually searching for names in lists and datasets, or using public search engines such as Google or Bing to search customer names with the aim of identifying potential risk. Manual screening may generate usable risk data but is limited in a number of important ways. For example, a search engine’s algorithm may deliver inconsistent or incomplete results, de-prioritise critical information, or block some results under regional data laws. Manual searches may also be time-consuming and vulnerable to human error, especially in cases where large numbers of names must be checked.  

Automated Screening

Firms can automate their AML name screening with software tools that are capable of searching through vast amounts of structured and unstructured data with speed and accuracy, reducing the potential for human error. Automated name screening tools allow compliance teams to tailor their searches, review thousands of global data sources in seconds, and then categorise and analyse that data to facilitate stronger decision-making. 

AML Risk Data Sources

The AML name screening process typically captures risk data from the following sources: 

  • Economic sanctions lists featuring the names of individuals, organisations and countries subject to economic sanctions imposed by governments. 
  • Politically exposed person (PEP) lists featuring the names of elected and unelected officials such as politicians, government officials, members of the military, and so on. 
  • Government watchlists featuring the names of persons known to pose a financial criminal risk. 
  • Adverse media sources including established news organisations, blogs, websites, forums, and social media posts.

Why is AML Name Screening Important?

Most jurisdictions set out risk-based AML compliance requirements, which makes name screening an essential part of an effective AML solution, and critical to avoiding costly regulatory penalties. 

Beyond regulatory obligations, name screening has a significant and meaningful impact in the global fight against money laundering. The value of name screening lies in both the quantity and quality of risk data that it can provide. Vital risk information gained from sanctions lists, PEP lists and watchlists can be enhanced and given additional context from adverse media results. For example, a firm may discover a news story about a customer’s involvement in a foreign money laundering investigation, containing information that may not have been reported by domestic outlets, and which may not be officially confirmed for months. Informed by that screening data, firms can adjust the customer’s AML risk profile and take appropriate action to avoid a compliance violation.  

Global Screening Challenges

Although it is an indispensable part of modern compliance, global AML name screening can present administrative and practical challenges. The most common include:

Data Volume

The sheer amount of data involved in AML name screening can be overwhelming. Firms must consider their search parameters carefully, taking into account the regions and languages in which searches should be conducted, and which watchlists or news publications they need to search. Certain searches may generate a huge amount of alerts, including false positives and redundant duplicate stories, all of which need remediation.

Data Quality

Not all risk data is equal. Information from low-credibility sources, such as blogs, forums, and social media posts, is typically less reliable than information from sanctions lists, PEP lists, watchlists, and established publications such as international news organisations. The distinction between low and high quality data may be more challenging for searches carried out in foreign languages. 

Language, Spelling, and Naming

Global name searches may struggle to account for the nuances of foreign languages, including naming and spelling conventions. Some cultures reverse the first name-surname order, for example, use prefixes before names, or approximate English spelling translations. Similarly, some data sources may use non-Latinate characters, such as Cyrillic or Arabic. 

Aliases, Nicknames and Similar Names

Searches may misidentify customers based on the use of aliases, nicknames, and similar or exact-match names. In searches of Western news stories, for example, the name “John Smith” would, without added contextual input, generate a huge amount of similar or exact-match name alerts, while customers that use nicknames or middle-names when signing up for services may also end up confusing search tools. On the other hand, criminals may actively try to evade searches by using aliases. 

AML Name Screening Best Practices

AML name screening is challenging, but firms can streamline their process by applying the following best practices as they develop and use their search solutions.

Optimise CDD

Firms should apply robust customer due diligence (CDD) measures during onboarding to verify their customers’ identities, including collecting official identifying documents such as passports. Effective CDD enriches customer risk profiles with contextual information which can, in turn, help compliance teams clarify name screening data where ambiguities and false positives emerge.

Automate Where Possible

Automation allows firms to build speed and accuracy into their AML screening process, accomplishing in seconds what would have previously taken hours to complete, and without the same potential for human error. Automated screening tools offer an array of peripheral benefits – solutions can be tailored to a firm’s business needs and risk appetite, and scaled to accommodate business growth. Automated solutions can also integrate emerging innovations, and account for multi-language screening challenges.

Risk Categorisation

It is important to implement a screening solution capable of discerning different types of risk from the content it targets, and categorising that information accordingly. Customers involved in potential financial crimes (such as fraud) may pose a different level of money laundering risk than customers involved in narcotics offences, for example, and that nuance can help firms clarify, and deploy a more efficient compliance response. 

Ongoing Monitoring 

AML risk screening should not become a box-ticking exercise. Customer risk profiles can change quickly as a result of elections, regulatory changes, or geopolitical events such as the conflict in Ukraine, and firms must be ready to adapt to changes in that environment. Screening solutions should continuously monitor for changes, and firms should be proactive in seeking out and capturing new risk data. 

Integrate AI

AI has become a powerful weapon in the fight against financial crime. The emergence of generative AI and large language models (LLMs) is particularly valuable for name screening since it enables firms to analyse unstructured data with unprecedented speed and accuracy, across numerous language systems. AI technology is also capable of discerning different levels of risk, identifying duplicate information, and recognising non-Western characters and other translation issues. 

Next Generation Name Screening 

Build next generation automated name screening into your AML solution with Ripjar’s Labyrinth Screening platform. Powered by AI technology, Labyrinth Screening is capable of searching thousands of global sanctions lists, PEP lists, watchlists and adverse media sources in seconds, to deliver actionable financial intelligence that enables firms to stay ahead of regulatory risk. 

Labyrinth Screening is designed to supercharge the name screening process and dramatically reduce assessment times. The platform includes Ripjar’s AI Risk Profiles tool to help teams extract only the most risk-relevant information from large volumes of data, and AI Summaries, a generative AI tool that adds concise prose summaries of that data to each customer profile.  

Discover Ripjar’s name screening solution

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